| { |
| "2201.11903": { |
| "arxivId": "2201.11903", |
| "title": "Chain of Thought Prompting Elicits Reasoning in Large Language Models" |
| }, |
| "2005.11401": { |
| "arxivId": "2005.11401", |
| "title": "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" |
| }, |
| "2009.03300": { |
| "arxivId": "2009.03300", |
| "title": "Measuring Massive Multitask Language Understanding" |
| }, |
| "2110.14168": { |
| "arxivId": "2110.14168", |
| "title": "Training verifiers to solve math word problems" |
| }, |
| "2304.03442": { |
| "arxivId": "2304.03442", |
| "title": "Generative agents: Interactive simulacra of human behavior" |
| }, |
| "2305.10601": { |
| "arxivId": "2305.10601", |
| "title": "Tree of thoughts: Deliberate problem solving with large language models" |
| }, |
| "2312.10997": { |
| "arxivId": "2312.10997", |
| "title": "Retrieval-Augmented Generation for Large Language Models: A Survey" |
| }, |
| "2303.11366": { |
| "arxivId": "2303.11366", |
| "title": "Reflexion: an autonomous agent with dynamic memory and self-reflection" |
| }, |
| "2308.11432": { |
| "arxivId": "2308.11432", |
| "title": "A survey on large language model based autonomous agents" |
| }, |
| "2101.02235": { |
| "arxivId": "2101.02235", |
| "title": "Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies" |
| }, |
| "2309.07864": { |
| "arxivId": "2309.07864", |
| "title": "The Rise and Potential of Large Language Model Based Agents: A Survey" |
| }, |
| "2308.00352": { |
| "arxivId": "2308.00352", |
| "title": "MetaGPT: Meta programming for multi-agent collaborative framework" |
| }, |
| "2308.08155": { |
| "arxivId": "2308.08155", |
| "title": "AutoGen: Enabling next-gen LLM applications via multi-agent conversation framework" |
| }, |
| "2305.14325": { |
| "arxivId": "2305.14325", |
| "title": "Improving factuality and reasoning in language models through multiagent debate" |
| }, |
| "2210.02406": { |
| "arxivId": "2210.02406", |
| "title": "Decomposed Prompting: A Modular Approach for Solving Complex Tasks" |
| }, |
| "2311.05232": { |
| "arxivId": "2311.05232", |
| "title": "A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions" |
| }, |
| "2308.07201": { |
| "arxivId": "2308.07201", |
| "title": "ChatEval: Towards better LLM-based evaluators through multi-agent debate" |
| }, |
| "2303.17760": { |
| "arxivId": "2303.17760", |
| "title": "CAMEL: Communicative Agents for \"Mind\" Exploration of Large Scale Language Model Society" |
| }, |
| "2208.10264": { |
| "arxivId": "2208.10264", |
| "title": "Using large language models to simulate multiple humans and replicate human subject studies" |
| }, |
| "2307.07924": { |
| "arxivId": "2307.07924", |
| "title": "Communicative agents for software development" |
| }, |
| "2208.04024": { |
| "arxivId": "2208.04024", |
| "title": "Social simulacra: Creating populated prototypes for social computing systems" |
| }, |
| "2304.07590": { |
| "arxivId": "2304.07590", |
| "title": "Self-collaboration code generation via ChatGPT" |
| }, |
| "2305.03514": { |
| "arxivId": "2305.03514", |
| "title": "Can large language models transform computational social science?" |
| }, |
| "2308.10848": { |
| "arxivId": "2308.10848", |
| "title": "AgentVerse: Facilitating multi-agent collaboration and exploring emergent behaviors in agents" |
| }, |
| "2301.07543": { |
| "arxivId": "2301.07543", |
| "title": "Large language models as simulated economic agents: What can we learn from homo silicus?" |
| }, |
| "2309.04658": { |
| "arxivId": "2309.04658", |
| "title": "Exploring large language models for communication games: An empirical study on werewolf" |
| }, |
| "2307.02485": { |
| "arxivId": "2307.02485", |
| "title": "Building cooperative embodied agents modularly with large language models" |
| }, |
| "2309.02427": { |
| "arxivId": "2309.02427", |
| "title": "Cognitive architectures for language agents" |
| }, |
| "2305.16867": { |
| "arxivId": "2305.16867", |
| "title": "Playing repeated games with large language models" |
| }, |
| "2304.08244": { |
| "arxivId": "2304.08244", |
| "title": "API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs" |
| }, |
| "2310.02170": { |
| "arxivId": "2310.02170", |
| "title": "Dynamic LLM-agent network: An LLM-agent collaboration framework with agent team optimization" |
| }, |
| "2307.14984": { |
| "arxivId": "2307.14984", |
| "title": "S3: Social-network simulation system with large language model-empowered agents" |
| }, |
| "2311.10537": { |
| "arxivId": "2311.10537", |
| "title": "MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning" |
| }, |
| "2310.02124": { |
| "arxivId": "2310.02124", |
| "title": "Exploring collaboration mechanisms for LLM agents: A social psychology view" |
| }, |
| "2307.04738": { |
| "arxivId": "2307.04738", |
| "title": "RoCo: Dialectic multi-robot collaboration with large language models" |
| }, |
| "2103.04044": { |
| "arxivId": "2103.04044", |
| "title": "Putting humans in the natural language processing loop: A survey" |
| }, |
| "2310.11667": { |
| "arxivId": "2310.11667", |
| "title": "SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents" |
| }, |
| "2309.07870": { |
| "arxivId": "2309.07870", |
| "title": "Agents: An open-source framework for autonomous language agents" |
| }, |
| "2311.17227": { |
| "arxivId": "2311.17227", |
| "title": "War and Peace (WarAgent): Large Language Model-based Multi-Agent Simulation of World Wars" |
| }, |
| "2310.06500": { |
| "arxivId": "2310.06500", |
| "title": "MetaAgents: Simulating interactions of human behaviors for LLM-based task-oriented coordination via collaborative generative agents" |
| }, |
| "2312.13010": { |
| "arxivId": "2312.13010", |
| "title": "AgentCoder: Multi-Agent-based Code Generation with Iterative Testing and Optimisation" |
| }, |
| "2310.10634": { |
| "arxivId": "2310.10634", |
| "title": "OpenAgents: An open platform for language agents in the wild" |
| }, |
| "2302.00763": { |
| "arxivId": "2302.00763", |
| "title": "Collaborating with language models for embodied reasoning" |
| }, |
| "2307.15810": { |
| "arxivId": "2307.15810", |
| "title": "Understanding the benefits and challenges of using large language model-based conversational agents for mental well-being support" |
| }, |
| "2309.17288": { |
| "arxivId": "2309.17288", |
| "title": "AutoAgents: A Framework for Automatic Agent Generation" |
| }, |
| "2310.18940": { |
| "arxivId": "2310.18940", |
| "title": "Language Agents with Reinforcement Learning for Strategic Play in the Werewolf Game" |
| }, |
| "2308.11339": { |
| "arxivId": "2308.11339", |
| "title": "ProAgent: Building Proactive Cooperative Agents with Large Language Models" |
| }, |
| "2309.09971": { |
| "arxivId": "2309.09971", |
| "title": "MindAgent: Emergent gaming interaction" |
| }, |
| "2310.01320": { |
| "arxivId": "2310.01320", |
| "title": "Avalon's Game of Thoughts: Battle Against Deception through Recursive Contemplation" |
| }, |
| "2309.15943": { |
| "arxivId": "2309.15943", |
| "title": "Scalable Multi-Robot Collaboration with Large Language Models: Centralized or Decentralized Systems?" |
| }, |
| "2310.17512": { |
| "arxivId": "2310.17512", |
| "title": "CompeteAI: Understanding the Competition Behaviors in Large Language Model-based Agents" |
| }, |
| "2312.05488": { |
| "arxivId": "2312.05488", |
| "title": "Can Large Language Models Serve as Rational Players in Game Theory? A Systematic Analysis" |
| }, |
| "2305.18365": { |
| "arxivId": "2305.18365", |
| "title": "What indeed can GPT models do in chemistry? A comprehensive benchmark on eight tasks" |
| }, |
| "2310.10701": { |
| "arxivId": "2310.10701", |
| "title": "Theory of Mind for Multi-Agent Collaboration via Large Language Models" |
| }, |
| "2305.11595": { |
| "arxivId": "2305.11595", |
| "title": "Examining the inter-consistency of large language models: An in-depth analysis via debate" |
| }, |
| "2310.09233": { |
| "arxivId": "2310.09233", |
| "title": "AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems" |
| }, |
| "2310.10436": { |
| "arxivId": "2310.10436", |
| "title": "Large Language Model-Empowered Agents for Simulating Macroeconomic Activities" |
| }, |
| "2305.14386": { |
| "arxivId": "2305.14386", |
| "title": "Let GPT be a Math Tutor: Teaching Math Word Problem Solvers with Customized Exercise Generation" |
| }, |
| "2307.06187": { |
| "arxivId": "2307.06187", |
| "title": "Self-Adaptive Large Language Model (LLM)-Based Multiagent Systems" |
| }, |
| "2310.10108": { |
| "arxivId": "2310.10108", |
| "title": "On generative agents in recommendation" |
| }, |
| "2307.04986": { |
| "arxivId": "2307.04986", |
| "title": "Epidemic modeling with generative agents" |
| }, |
| "2308.03427": { |
| "arxivId": "2308.03427", |
| "title": "TPTU: Task planning and tool usage of large language model-based AI agents" |
| }, |
| "2309.03736": { |
| "arxivId": "2309.03736", |
| "title": "TradingGPT: Multi-agent system with layered memory and distinct characters for enhanced financial trading performance" |
| }, |
| "2310.02172": { |
| "arxivId": "2310.02172", |
| "title": "Lyfe agents: Generative agents for low-cost real-time social interactions" |
| }, |
| "2311.08562": { |
| "arxivId": "2311.08562", |
| "title": "MAgIC: Investigation of Large Language Model Powered Multi-Agent in Cognition, Adaptability, Rationality and Collaboration" |
| }, |
| "2310.07937": { |
| "arxivId": "2310.07937", |
| "title": "Co-NavGPT: Multi-Robot Cooperative Visual Semantic Navigation using Large Language Models" |
| }, |
| "2310.08901": { |
| "arxivId": "2310.08901", |
| "title": "Welfare diplomacy: Benchmarking language model cooperation" |
| }, |
| "2308.03313": { |
| "arxivId": "2308.03313", |
| "title": "Quantifying the impact of large language models on collective opinion dynamics" |
| }, |
| "2307.07871": { |
| "arxivId": "2307.07871", |
| "title": "The SocialAI School: Insights from developmental psychology towards artificial socio-cultural agents" |
| }, |
| "2310.20151": { |
| "arxivId": "2310.20151", |
| "title": "Multi-Agent Consensus Seeking via Large Language Models" |
| }, |
| "2307.10337": { |
| "arxivId": "2307.10337", |
| "title": "Are you in a masquerade? Exploring the behavior and impact of large language model driven social bots in online social networks" |
| }, |
| "1612.00796": { |
| "arxivId": "1612.00796", |
| "title": "Overcoming catastrophic forgetting in neural networks" |
| }, |
| "2104.08691": { |
| "arxivId": "2104.08691", |
| "title": "The Power of Scale for Parameter-Efficient Prompt Tuning" |
| }, |
| "2004.07780": { |
| "arxivId": "2004.07780", |
| "title": "Shortcut learning in deep neural networks" |
| }, |
| "2202.03629": { |
| "arxivId": "2202.03629", |
| "title": "Survey of hallucination in natural language generation" |
| }, |
| "2212.13138": { |
| "arxivId": "2212.13138", |
| "title": "Large language models encode clinical knowledge" |
| }, |
| "2109.07958": { |
| "arxivId": "2109.07958", |
| "title": "TruthfulQA: Measuring How Models Mimic Human Falsehoods" |
| }, |
| "2302.04023": { |
| "arxivId": "2302.04023", |
| "title": "A multitask, multilingual, multimodal evaluation of chatgpt on reasoning, hallucination, and interactivity" |
| }, |
| "2104.08786": { |
| "arxivId": "2104.08786", |
| "title": "Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity" |
| }, |
| "2106.10199": { |
| "arxivId": "2106.10199", |
| "title": "BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models" |
| }, |
| "2306.02858": { |
| "arxivId": "2306.02858", |
| "title": "Video-LLaMA: An instruction-tuned audio-visual language model for video understanding" |
| }, |
| "2005.00052": { |
| "arxivId": "2005.00052", |
| "title": "MAD-X: An Adapter-based Framework for Multi-task Cross-lingual Transfer" |
| }, |
| "2202.13169": { |
| "arxivId": "2202.13169", |
| "title": "A systematic evaluation of large language models of code" |
| }, |
| "2212.10403": { |
| "arxivId": "2212.10403", |
| "title": "Towards reasoning in large language models: A survey" |
| }, |
| "2209.06899": { |
| "arxivId": "2209.06899", |
| "title": "Out of one, many: Using language models to simulate human samples" |
| }, |
| "2308.09687": { |
| "arxivId": "2308.09687", |
| "title": "Graph of thoughts: Solving elaborate problems with large language models" |
| }, |
| "2108.11896": { |
| "arxivId": "2108.11896", |
| "title": "A Survey on Automated Fact-Checking" |
| }, |
| "2010.15980": { |
| "arxivId": "2010.15980", |
| "title": "Eliciting Knowledge from Language Models Using Automatically Generated Prompts" |
| }, |
| "2305.14992": { |
| "arxivId": "2305.14992", |
| "title": "Reasoning with language model is planning with world model" |
| }, |
| "2112.07916": { |
| "arxivId": "2112.07916", |
| "title": "LongT5: Efficient text-to-text transformer for long sequences" |
| }, |
| "2309.00770": { |
| "arxivId": "2309.00770", |
| "title": "Bias and Fairness in Large Language Models: A Survey" |
| }, |
| "2212.10509": { |
| "arxivId": "2212.10509", |
| "title": "Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions" |
| }, |
| "2304.05376": { |
| "arxivId": "2304.05376", |
| "title": "Augmenting large language models with chemistry tools" |
| }, |
| "2305.05658": { |
| "arxivId": "2305.05658", |
| "title": "TidyBot: Personalized Robot Assistance with Large Language Models" |
| }, |
| "2210.08726": { |
| "arxivId": "2210.08726", |
| "title": "RARR: Researching and Revising What Language Models Say, Using Language Models" |
| }, |
| "2305.00447": { |
| "arxivId": "2305.00447", |
| "title": "TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation" |
| }, |
| "2305.13172": { |
| "arxivId": "2305.13172", |
| "title": "Editing large language models: Problems, methods, and opportunities" |
| }, |
| "2305.11000": { |
| "arxivId": "2305.11000", |
| "title": "SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities" |
| }, |
| "2104.06683": { |
| "arxivId": "2104.06683", |
| "title": "The Curious Case of Hallucinations in Neural Machine Translation" |
| }, |
| "2210.07128": { |
| "arxivId": "2210.07128", |
| "title": "Language models of code are few-shot commonsense learners" |
| }, |
| "2303.16434": { |
| "arxivId": "2303.16434", |
| "title": "TaskMatrix.AI: Completing tasks by connecting foundation models with millions of APIs" |
| }, |
| "2301.13379": { |
| "arxivId": "2301.13379", |
| "title": "Faithful chain-of-thought reasoning" |
| }, |
| "2305.02182": { |
| "arxivId": "2305.02182", |
| "title": "Uncovering ChatGPT\u2019s Capabilities in Recommender Systems" |
| }, |
| "2304.01933": { |
| "arxivId": "2304.01933", |
| "title": "LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models" |
| }, |
| "2304.12995": { |
| "arxivId": "2304.12995", |
| "title": "AudioGPT: Understanding and generating speech, music, sound, and talking head" |
| }, |
| "2305.06983": { |
| "arxivId": "2305.06983", |
| "title": "Active Retrieval Augmented Generation" |
| }, |
| "2305.14739": { |
| "arxivId": "2305.14739", |
| "title": "Trusting Your Evidence: Hallucinate Less with Context-aware Decoding" |
| }, |
| "1910.10486": { |
| "arxivId": "1910.10486", |
| "title": "Does Gender Matter? Towards Fairness in Dialogue Systems" |
| }, |
| "2305.15294": { |
| "arxivId": "2305.15294", |
| "title": "Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy" |
| }, |
| "2308.14921": { |
| "arxivId": "2308.14921", |
| "title": "Gender bias and stereotypes in Large Language Models" |
| }, |
| "2308.10144": { |
| "arxivId": "2308.10144", |
| "title": "Expel: LLM agents are experiential learners" |
| }, |
| "2309.11495": { |
| "arxivId": "2309.11495", |
| "title": "Chain-of-verification reduces hallucination in large language models" |
| }, |
| "2402.01680": { |
| "arxivId": "2402.01680", |
| "title": "Large Language Model based Multi-Agents: A Survey of Progress and Challenges" |
| }, |
| "2311.00423": { |
| "arxivId": "2311.00423", |
| "title": "LLMRec: Large Language Models with Graph Augmentation for Recommendation" |
| }, |
| "2310.09219": { |
| "arxivId": "2310.09219", |
| "title": "\"Kelly is a Warm Person, Joseph is a Role Model\": Gender Biases in LLM-Generated Reference Letters" |
| }, |
| "2308.08742": { |
| "arxivId": "2308.08742", |
| "title": "PMET: Precise Model Editing in a Transformer" |
| }, |
| "2305.13661": { |
| "arxivId": "2305.13661", |
| "title": "On the Risk of Misinformation Pollution with Large Language Models" |
| }, |
| "2305.16843": { |
| "arxivId": "2305.16843", |
| "title": "Randomized positional encodings boost length generalization of transformers" |
| }, |
| "2307.05300": { |
| "arxivId": "2307.05300", |
| "title": "Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration" |
| }, |
| "2305.10250": { |
| "arxivId": "2305.10250", |
| "title": "MemoryBank: Enhancing large language models with long-term memory" |
| }, |
| "2203.07540": { |
| "arxivId": "2203.07540", |
| "title": "Scienceworld: Is your agent smarter than a 5th grader?" |
| }, |
| "2208.11857": { |
| "arxivId": "2208.11857", |
| "title": "Shortcut Learning of Large Language Models in Natural Language Understanding" |
| }, |
| "1802.01604": { |
| "arxivId": "1802.01604", |
| "title": "Learning from richer human guidance: Augmenting comparison-based learning with feature queries" |
| }, |
| "2308.14296": { |
| "arxivId": "2308.14296", |
| "title": "RecMind: Large Language Model Powered Agent For Recommendation" |
| }, |
| "2303.09752": { |
| "arxivId": "2303.09752", |
| "title": "ColT5: Faster long-range transformers with conditional computation" |
| }, |
| "2205.12586": { |
| "arxivId": "2205.12586", |
| "title": "Perturbation Augmentation for Fairer NLP" |
| }, |
| "2204.02515": { |
| "arxivId": "2204.02515", |
| "title": "Inferring rewards from language in context" |
| }, |
| "2309.09709": { |
| "arxivId": "2309.09709", |
| "title": "CATR: Combinatorial-Dependence Audio-Queried Transformer for Audio-Visual Video Segmentation" |
| }, |
| "2305.17256": { |
| "arxivId": "2305.17256", |
| "title": "Large Language Models Can be Lazy Learners: Analyze Shortcuts in In-Context Learning" |
| }, |
| "2308.00121": { |
| "arxivId": "2308.00121", |
| "title": "Getting pwn\u2019d by AI: Penetration Testing with Large Language Models" |
| }, |
| "2311.13314": { |
| "arxivId": "2311.13314", |
| "title": "Mitigating Large Language Model Hallucinations via Autonomous Knowledge Graph-based Retrofitting" |
| }, |
| "2211.07067": { |
| "arxivId": "2211.07067", |
| "title": "Retrieval-Augmented Generative Question Answering for Event Argument Extraction" |
| }, |
| "2310.13243": { |
| "arxivId": "2310.13243", |
| "title": "Open-source Large Language Models are Strong Zero-shot Query Likelihood Models for Document Ranking" |
| }, |
| "2305.17590": { |
| "arxivId": "2305.17590", |
| "title": "Integrating action knowledge and LLMs for task planning and situation handling in open worlds" |
| }, |
| "2201.08643": { |
| "arxivId": "2201.08643", |
| "title": "Text Style Transfer for Bias Mitigation using Masked Language Modeling" |
| }, |
| "2109.11708": { |
| "arxivId": "2109.11708", |
| "title": "Detect and Perturb: Neutral Rewriting of Biased and Sensitive Text via Gradient-based Decoding" |
| }, |
| "2104.07429": { |
| "arxivId": "2104.07429", |
| "title": "First the Worst: Finding Better Gender Translations During Beam Search" |
| }, |
| "2401.02500": { |
| "arxivId": "2401.02500", |
| "title": "On the Prospects of Incorporating Large Language Models (LLMs) in Automated Planning and Scheduling (APS)" |
| }, |
| "2308.01542": { |
| "arxivId": "2308.01542", |
| "title": "Memory sandbox: Transparent and interactive memory management for conversational agents" |
| }, |
| "2310.18347": { |
| "arxivId": "2310.18347", |
| "title": "PRCA: Fitting Black-Box Large Language Models for Retrieval Question Answering via Pluggable Reward-Driven Contextual Adapter" |
| }, |
| "2305.15212": { |
| "arxivId": "2305.15212", |
| "title": "Towards Adaptive Prefix Tuning for Parameter-Efficient Language Model Fine-tuning" |
| }, |
| "2305.02012": { |
| "arxivId": "2305.02012", |
| "title": "A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME" |
| }, |
| "2211.11109": { |
| "arxivId": "2211.11109", |
| "title": "Deep Learning on a Healthy Data Diet: Finding Important Examples for Fairness" |
| }, |
| "2311.08648": { |
| "arxivId": "2311.08648", |
| "title": "Explore Spurious Correlations at the Concept Level in Language Models for Text Classification" |
| }, |
| "2305.19912": { |
| "arxivId": "2305.19912", |
| "title": "Structure-Aware Language Model Pretraining Improves Dense Retrieval on Structured Data" |
| }, |
| "2210.05499": { |
| "arxivId": "2210.05499", |
| "title": "Capturing global structural information in long document question answering with compressive graph selector network" |
| }, |
| "2203.14207": { |
| "arxivId": "2203.14207", |
| "title": "Text Adversarial Purification as Defense against Adversarial Attacks" |
| }, |
| "1706.03762": { |
| "arxivId": "1706.03762", |
| "title": "Attention is all you need" |
| }, |
| "2307.09288": { |
| "arxivId": "2307.09288", |
| "title": "Llama 2: Open Foundation and Fine-Tuned Chat Models" |
| }, |
| "2106.09685": { |
| "arxivId": "2106.09685", |
| "title": "LoRA: Low-Rank Adaptation of Large Language Models" |
| }, |
| "2204.02311": { |
| "arxivId": "2204.02311", |
| "title": "PaLM: Scaling Language Modeling with Pathways" |
| }, |
| "1706.02275": { |
| "arxivId": "1706.02275", |
| "title": "Multi-agent actor-critic for mixed cooperative-competitive environments" |
| }, |
| "2107.03374": { |
| "arxivId": "2107.03374", |
| "title": "Evaluating large language models trained on code" |
| }, |
| "1706.03741": { |
| "arxivId": "1706.03741", |
| "title": "Deep reinforcement learning from human preferences" |
| }, |
| "1605.06676": { |
| "arxivId": "1605.06676", |
| "title": "Learning to Communicate with Deep Multi-Agent Reinforcement Learning" |
| }, |
| "2204.01691": { |
| "arxivId": "2204.01691", |
| "title": "Do as I can, not as I say: Grounding language in robotic affordances" |
| }, |
| "2108.07732": { |
| "arxivId": "2108.07732", |
| "title": "Program synthesis with large language models" |
| }, |
| "2303.03378": { |
| "arxivId": "2303.03378", |
| "title": "Palm-e: An embodied multimodal language model" |
| }, |
| "1605.07736": { |
| "arxivId": "1605.07736", |
| "title": "Learning Multiagent Communication with Backpropagation" |
| }, |
| "2103.03874": { |
| "arxivId": "2103.03874", |
| "title": "Measuring Mathematical Problem Solving With the MATH Dataset" |
| }, |
| "2103.01955": { |
| "arxivId": "2103.01955", |
| "title": "The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games" |
| }, |
| "2201.07207": { |
| "arxivId": "2201.07207", |
| "title": "Language models as zero-shot planners: Extracting actionable knowledge for embodied agents" |
| }, |
| "1610.03295": { |
| "arxivId": "1610.03295", |
| "title": "Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving" |
| }, |
| "1905.05408": { |
| "arxivId": "1905.05408", |
| "title": "QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning" |
| }, |
| "1703.04908": { |
| "arxivId": "1703.04908", |
| "title": "Emergence of Grounded Compositional Language in Multi-Agent Populations" |
| }, |
| "1812.11794": { |
| "arxivId": "1812.11794", |
| "title": "Deep Reinforcement Learning for Multiagent Systems: A Review of Challenges, Solutions, and Applications" |
| }, |
| "2003.08839": { |
| "arxivId": "2003.08839", |
| "title": "Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning" |
| }, |
| "1810.05587": { |
| "arxivId": "1810.05587", |
| "title": "A survey and critique of multiagent deep reinforcement learning" |
| }, |
| "1805.07733": { |
| "arxivId": "1805.07733", |
| "title": "Learning Attentional Communication for Multi-Agent Cooperation" |
| }, |
| "2008.02275": { |
| "arxivId": "2008.02275", |
| "title": "Aligning AI With Shared Human Values" |
| }, |
| "2008.01062": { |
| "arxivId": "2008.01062", |
| "title": "QPLEX: Duplex Dueling Multi-Agent Q-Learning" |
| }, |
| "1810.11187": { |
| "arxivId": "1810.11187", |
| "title": "TarMAC: Targeted Multi-Agent Communication" |
| }, |
| "2006.10800": { |
| "arxivId": "2006.10800", |
| "title": "Weighted QMIX: Expanding Monotonic Value Function Factorisation" |
| }, |
| "2109.08238": { |
| "arxivId": "2109.08238", |
| "title": "Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AI" |
| }, |
| "1906.03926": { |
| "arxivId": "1906.03926", |
| "title": "A Survey of Reinforcement Learning Informed by Natural Language" |
| }, |
| "1706.06122": { |
| "arxivId": "1706.06122", |
| "title": "VAIN: Attentional Multi-agent Predictive Modeling" |
| }, |
| "1906.07343": { |
| "arxivId": "1906.07343", |
| "title": "Language as an Abstraction for Hierarchical Deep Reinforcement Learning" |
| }, |
| "2006.02419": { |
| "arxivId": "2006.02419", |
| "title": "Emergent Multi-Agent Communication in the Deep Learning Era" |
| }, |
| "2007.12322": { |
| "arxivId": "2007.12322", |
| "title": "Off-Policy Multi-Agent Decomposed Policy Gradients" |
| }, |
| "2304.01904": { |
| "arxivId": "2304.01904", |
| "title": "REFINER: Reasoning Feedback on Intermediate Representations" |
| }, |
| "2010.09890": { |
| "arxivId": "2010.09890", |
| "title": "Watch-And-Help: A Challenge for Social Perception and Human-AI Collaboration" |
| }, |
| "1910.01465": { |
| "arxivId": "1910.01465", |
| "title": "Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Critics" |
| }, |
| "1701.04079": { |
| "arxivId": "1701.04079", |
| "title": "Agent-Agnostic Human-in-the-Loop Reinforcement Learning" |
| }, |
| "2308.02151": { |
| "arxivId": "2308.02151", |
| "title": "Retroformer: Retrospective large language agents with policy gradient optimization" |
| }, |
| "2402.02242": { |
| "arxivId": "2402.02242", |
| "title": "Parameter-Efficient Fine-Tuning for Pre-Trained Vision Models: A Survey" |
| }, |
| "2311.05772": { |
| "arxivId": "2311.05772", |
| "title": "ADaPT: As-Needed Decomposition and Planning with Language Models" |
| }, |
| "2309.10062": { |
| "arxivId": "2309.10062", |
| "title": "SMART-LLM: Smart Multi-Agent Robot Task Planning using Large Language Models" |
| }, |
| "2209.15189": { |
| "arxivId": "2209.15189", |
| "title": "Learning by Distilling Context" |
| }, |
| "2008.06924": { |
| "arxivId": "2008.06924", |
| "title": "Inverse Reinforcement Learning with Natural Language Goals" |
| }, |
| "2312.08636": { |
| "arxivId": "2312.08636", |
| "title": "MmAP : Multi-modal Alignment Prompt for Cross-domain Multi-task Learning" |
| }, |
| "2312.08733": { |
| "arxivId": "2312.08733", |
| "title": "VMT-Adapter: Parameter-Efficient Transfer Learning for Multi-Task Dense Scene Understanding" |
| }, |
| "2403.17343": { |
| "arxivId": "2403.17343", |
| "title": "Residual-based Language Models are Free Boosters for Biomedical Imaging Tasks" |
| }, |
| "2212.10670": { |
| "arxivId": "2212.10670", |
| "title": "In-context Learning Distillation: Transferring Few-shot Learning Ability of Pre-trained Language Models" |
| }, |
| "2004.02780": { |
| "arxivId": "2004.02780", |
| "title": "Networked Multi-Agent Reinforcement Learning with Emergent Communication" |
| }, |
| "2404.00282": { |
| "arxivId": "2404.00282", |
| "title": "Survey on Large Language Model-Enhanced Reinforcement Learning: Concept, Taxonomy, and Methods" |
| }, |
| "2403.11807": { |
| "arxivId": "2403.11807", |
| "title": "How Far Are We on the Decision-Making of LLMs? Evaluating LLMs' Gaming Ability in Multi-Agent Environments" |
| }, |
| "2403.13786": { |
| "arxivId": "2403.13786", |
| "title": "Chain-of-Interaction: Enhancing Large Language Models for Psychiatric Behavior Understanding by Dyadic Contexts" |
| }, |
| "2403.19839": { |
| "arxivId": "2403.19839", |
| "title": "The New Agronomists: Language Models are Experts in Crop Management" |
| }, |
| "2403.12482": { |
| "arxivId": "2403.12482", |
| "title": "Embodied LLM Agents Learn to Cooperate in Organized Teams" |
| }, |
| "2112.10752": { |
| "arxivId": "2112.10752", |
| "title": "High-Resolution Image Synthesis with Latent Diffusion Models" |
| }, |
| "2304.02643": { |
| "arxivId": "2304.02643", |
| "title": "Segment Anything" |
| }, |
| "2201.12086": { |
| "arxivId": "2201.12086", |
| "title": "BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation" |
| }, |
| "2301.12597": { |
| "arxivId": "2301.12597", |
| "title": "BLIP-2: Bootstrapping language-image pre-training with frozen image encoders and large language models" |
| }, |
| "2305.06500": { |
| "arxivId": "2305.06500", |
| "title": "InstructBLIP: Towards general-purpose vision-language models with instruction tuning" |
| }, |
| "2302.04761": { |
| "arxivId": "2302.04761", |
| "title": "Toolformer: Language models can teach themselves to use tools" |
| }, |
| "2303.05499": { |
| "arxivId": "2303.05499", |
| "title": "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection" |
| }, |
| "2303.17580": { |
| "arxivId": "2303.17580", |
| "title": "HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face" |
| }, |
| "2303.04671": { |
| "arxivId": "2303.04671", |
| "title": "Visual ChatGPT: Talking, drawing and editing with visual foundation models" |
| }, |
| "2307.16789": { |
| "arxivId": "2307.16789", |
| "title": "ToolLLM: Facilitating large language models to master 16000+ real-world APIs" |
| }, |
| "2303.08128": { |
| "arxivId": "2303.08128", |
| "title": "ViperGPT: Visual Inference via Python Execution for Reasoning" |
| }, |
| "2303.11381": { |
| "arxivId": "2303.11381", |
| "title": "MM-REACT: Prompting ChatGPT for multimodal reasoning and action" |
| }, |
| "2211.11559": { |
| "arxivId": "2211.11559", |
| "title": "Visual Programming: Compositional visual reasoning without training" |
| }, |
| "2203.11991": { |
| "arxivId": "2203.11991", |
| "title": "Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Framework" |
| }, |
| "2302.01560": { |
| "arxivId": "2302.01560", |
| "title": "Describe, explain, plan and select: Interactive planning with large language models enables open-world multi-task agents" |
| }, |
| "2304.09842": { |
| "arxivId": "2304.09842", |
| "title": "Chameleon: Plug-and-play compositional reasoning with large language models" |
| }, |
| "2305.18752": { |
| "arxivId": "2305.18752", |
| "title": "GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction" |
| }, |
| "2310.01415": { |
| "arxivId": "2310.01415", |
| "title": "GPT-Driver: Learning to Drive with GPT" |
| }, |
| "2307.07162": { |
| "arxivId": "2307.07162", |
| "title": "Drive Like a Human: Rethinking Autonomous Driving with Large Language Models" |
| }, |
| "2312.13771": { |
| "arxivId": "2312.13771", |
| "title": "AppAgent: Multimodal Agents as Smartphone Users" |
| }, |
| "2311.12983": { |
| "arxivId": "2311.12983", |
| "title": "GAIA: a benchmark for General AI Assistants" |
| }, |
| "2401.13649": { |
| "arxivId": "2401.13649", |
| "title": "VisualWebArena: Evaluating Multimodal Agents on Realistic Visual Web Tasks" |
| }, |
| "2311.05437": { |
| "arxivId": "2311.05437", |
| "title": "LLaVA-Plus: Learning to Use Tools for Creating Multimodal Agents" |
| }, |
| "2311.07562": { |
| "arxivId": "2311.07562", |
| "title": "GPT-4V in Wonderland: Large Multimodal Models for Zero-Shot Smartphone GUI Navigation" |
| }, |
| "2402.01622": { |
| "arxivId": "2402.01622", |
| "title": "TravelPlanner: A Benchmark for Real-World Planning with Language Agents" |
| }, |
| "2311.05997": { |
| "arxivId": "2311.05997", |
| "title": "JARVIS-1: Open-world multi-task agents with memory-augmented multimodal language models" |
| }, |
| "2101.06175": { |
| "arxivId": "2101.06175", |
| "title": "PaddleSeg: A High-Efficient Development Toolkit for Image Segmentation" |
| }, |
| "2311.05332": { |
| "arxivId": "2311.05332", |
| "title": "On the Road with GPT-4V(ision): Early Explorations of Visual-Language Model on Autonomous Driving" |
| }, |
| "2306.07691": { |
| "arxivId": "2306.07691", |
| "title": "StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training with Large Speech Language Models" |
| }, |
| "2309.11436": { |
| "arxivId": "2309.11436", |
| "title": "You Only Look at Screens: Multimodal Chain-of-Action Agents" |
| }, |
| "2306.08640": { |
| "arxivId": "2306.08640", |
| "title": "AssistGPT: A general multi-modal assistant that can plan, execute, inspect, and learn" |
| }, |
| "2310.16436": { |
| "arxivId": "2310.16436", |
| "title": "DDCoT: Duty-Distinct Chain-of-Thought Prompting for Multimodal Reasoning in Language Models" |
| }, |
| "2401.16158": { |
| "arxivId": "2401.16158", |
| "title": "Mobile-Agent: Autonomous Multi-Modal Mobile Device Agent with Visual Perception" |
| }, |
| "2111.00607": { |
| "arxivId": "2111.00607", |
| "title": "A Systematic Investigation of Commonsense Knowledge in Large Language Models" |
| }, |
| "2402.07456": { |
| "arxivId": "2402.07456", |
| "title": "OS-Copilot: Towards Generalist Computer Agents with Self-Improvement" |
| }, |
| "2308.06374": { |
| "arxivId": "2308.06374", |
| "title": "Large Language Models and Knowledge Graphs: Opportunities and Challenges" |
| }, |
| "2310.14414": { |
| "arxivId": "2310.14414", |
| "title": "Vision Language Models in Autonomous Driving and Intelligent Transportation Systems" |
| }, |
| "2310.10021": { |
| "arxivId": "2310.10021", |
| "title": "Bootstrap your own skills: Learning to solve new tasks with large language model guidance" |
| }, |
| "2310.01557": { |
| "arxivId": "2310.01557", |
| "title": "SmartPlay : A Benchmark for LLMs as Intelligent Agents" |
| }, |
| "2304.14407": { |
| "arxivId": "2304.14407", |
| "title": "ChatVideo: A Tracklet-centric Multimodal and Versatile Video Understanding System" |
| }, |
| "2309.17428": { |
| "arxivId": "2309.17428", |
| "title": "CRAFT: Customizing LLMs by creating and retrieving from specialized toolsets" |
| }, |
| "2310.15166": { |
| "arxivId": "2310.15166", |
| "title": "Large Language Models are Visual Reasoning Coordinators" |
| }, |
| "2308.15272": { |
| "arxivId": "2308.15272", |
| "title": "AutoDroid: LLM-powered Task Automation in Android" |
| }, |
| "2310.08588": { |
| "arxivId": "2310.08588", |
| "title": "Octopus: Embodied Vision-Language Programmer from Environmental Feedback" |
| }, |
| "2402.05930": { |
| "arxivId": "2402.05930", |
| "title": "WebLINX: Real-World Website Navigation with Multi-Turn Dialogue" |
| }, |
| "2309.11382": { |
| "arxivId": "2309.11382", |
| "title": "Discuss Before Moving: Visual Language Navigation via Multi-expert Discussions" |
| }, |
| "2311.00571": { |
| "arxivId": "2311.00571", |
| "title": "LLaVA-Interactive: An All-in-One Demo for Image Chat, Segmentation, Generation and Editing" |
| }, |
| "2304.07061": { |
| "arxivId": "2304.07061", |
| "title": "DroidBot-GPT: GPT-powered UI Automation for Android" |
| }, |
| "2310.09291": { |
| "arxivId": "2310.09291", |
| "title": "Vision-by-Language for Training-Free Compositional Image Retrieval" |
| }, |
| "2401.08392": { |
| "arxivId": "2401.08392", |
| "title": "DoraemonGPT: Toward Understanding Dynamic Scenes with Large Language Models" |
| }, |
| "2312.07472": { |
| "arxivId": "2312.07472", |
| "title": "MP5: A Multi-modal Open-ended Embodied System in Minecraft via Active Perception" |
| }, |
| "2307.14335": { |
| "arxivId": "2307.14335", |
| "title": "WavJourney: Compositional Audio Creation with Large Language Models" |
| }, |
| "2312.13108": { |
| "arxivId": "2312.13108", |
| "title": "ASSISTGUI: Task-Oriented Desktop Graphical User Interface Automation" |
| }, |
| "2311.16714": { |
| "arxivId": "2311.16714", |
| "title": "Embodied Multi-Modal Agent trained by an LLM from a Parallel TextWorld" |
| }, |
| "2311.15209": { |
| "arxivId": "2311.15209", |
| "title": "See and Think: Embodied Agent in Virtual Environment" |
| }, |
| "2312.10908": { |
| "arxivId": "2312.10908", |
| "title": "CLOVA: A Closed-LOop Visual Assistant with Tool Usage and Update" |
| }, |
| "2310.07343": { |
| "arxivId": "2310.07343", |
| "title": "How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances" |
| }, |
| "2310.12404": { |
| "arxivId": "2310.12404", |
| "title": "Loop Copilot: Conducting AI Ensembles for Music Generation and Iterative Editing" |
| }, |
| "2312.15918": { |
| "arxivId": "2312.15918", |
| "title": "Supervised Knowledge Makes Large Language Models Better In-context Learners" |
| }, |
| "cs/9605103": { |
| "arxivId": "cs/9605103", |
| "title": "Reinforcement learning: A survey" |
| }, |
| "2002.00444": { |
| "arxivId": "2002.00444", |
| "title": "Deep reinforcement learning for autonomous driving: A survey" |
| }, |
| "2303.18223": { |
| "arxivId": "2303.18223", |
| "title": "A survey of large language models" |
| }, |
| "2303.12712": { |
| "arxivId": "2303.12712", |
| "title": "Sparks of artificial general intelligence: Early experiments with GPT-4" |
| }, |
| "1810.04805": { |
| "arxivId": "1810.04805", |
| "title": "BERT: Pre-training of deep bidirectional transformers for language understanding" |
| }, |
| "2303.08774": { |
| "arxivId": "2303.08774", |
| "title": "GPT-4 technical report" |
| }, |
| "2305.17066": { |
| "arxivId": "2305.17066", |
| "title": "Mindstorms in natural language-based societies of mind" |
| }, |
| "2301.05327": { |
| "arxivId": "2301.05327", |
| "title": "Blind judgement: Agent-based supreme court modelling with GPT" |
| }, |
| "2112.15594": { |
| "arxivId": "2112.15594", |
| "title": "A neural network solves, explains, and generates university math problems by program synthesis and few-shot learning at human level" |
| }, |
| "2304.10750": { |
| "arxivId": "2304.10750", |
| "title": "Improving grounded language understanding in a collaborative environment by interacting with agents through help feedback" |
| }, |
| "2304.14354": { |
| "arxivId": "2304.14354", |
| "title": "Industrial engineering with large language models: A case study of chatgpt's performance on oil & gas problems" |
| }, |
| "2301.12050": { |
| "arxivId": "2301.12050", |
| "title": "Do Embodied Agents Dream of Pixelated Sheep?: Embodied Decision Making using Language Guided World Modelling" |
| }, |
| "2212.04088": { |
| "arxivId": "2212.04088", |
| "title": "LLM-planner: Few-shot grounded planning for embodied agents with large language models" |
| }, |
| "2305.16291": { |
| "arxivId": "2305.16291", |
| "title": "Voyager: An open-ended embodied agent with large language models" |
| }, |
| "2306.07929": { |
| "arxivId": "2306.07929", |
| "title": "Large language model is semi-parametric reinforcement learning agent" |
| }, |
| "2307.09668": { |
| "arxivId": "2307.09668", |
| "title": "Towards a unified agent with foundation models" |
| }, |
| "2304.05332": { |
| "arxivId": "2304.05332", |
| "title": "Emergent autonomous scientific research capabilities of large language models" |
| }, |
| "2304.14721": { |
| "arxivId": "2304.14721", |
| "title": "Towards autonomous system: flexible modular production system enhanced with large language model agents" |
| }, |
| "2210.00720": { |
| "arxivId": "2210.00720", |
| "title": "Complexity-based prompting for multi-step reasoning" |
| }, |
| "2210.03493": { |
| "arxivId": "2210.03493", |
| "title": "Automatic chain of thought prompting in large language models" |
| }, |
| "2205.11916": { |
| "arxivId": "2205.11916", |
| "title": "Large language models are zero-shot reasoners" |
| }, |
| "2203.11171": { |
| "arxivId": "2203.11171", |
| "title": "Self-consistency improves chain of thought reasoning in language models" |
| }, |
| "2205.10625": { |
| "arxivId": "2205.10625", |
| "title": "Least-to-most prompting enables complex reasoning in large language models" |
| }, |
| "2307.15337": { |
| "arxivId": "2307.15337", |
| "title": "Skeleton-of-thought: Large language models can do parallel decoding" |
| }, |
| "2304.09797": { |
| "arxivId": "2304.09797", |
| "title": "Progressive-hint prompting improves reasoning in large language models" |
| }, |
| "2303.17651": { |
| "arxivId": "2303.17651", |
| "title": "Self-refine: Iterative refinement with self-feedback" |
| }, |
| "2304.11477": { |
| "arxivId": "2304.11477", |
| "title": "LLM+P: empowering large language models with optimal planning proficiency" |
| }, |
| "2308.06391": { |
| "arxivId": "2308.06391", |
| "title": "Dynamic planning with a LLM" |
| }, |
| "2308.09830": { |
| "arxivId": "2308.09830", |
| "title": "Synergistic integration of large language models and cognitive architectures for robust AI: An exploratory analysis" |
| }, |
| "2305.17390": { |
| "arxivId": "2305.17390", |
| "title": "SwiftSage: A generative agent with fast and slow thinking for complex interactive tasks" |
| }, |
| "2301.00234": { |
| "arxivId": "2301.00234", |
| "title": "A survey for in-context learning" |
| }, |
| "2305.14909": { |
| "arxivId": "2305.14909", |
| "title": "Leveraging pre-trained large language models to construct and utilize world models for model-based task planning" |
| }, |
| "2305.14078": { |
| "arxivId": "2305.14078", |
| "title": "Large language models as commonsense knowledge for large-scale task planning" |
| }, |
| "2310.03051": { |
| "arxivId": "2310.03051", |
| "title": "How far are large language models from agents with theory-of-mind?" |
| }, |
| "2310.04406": { |
| "arxivId": "2310.04406", |
| "title": "Language agent tree search unifies reasoning acting and planning in language models" |
| }, |
| "2307.07697": { |
| "arxivId": "2307.07697", |
| "title": "Think-on-graph: Deep and responsible reasoning of large language model on knowledge graph" |
| }, |
| "2310.08560": { |
| "arxivId": "2310.08560", |
| "title": "MemGPT: Towards LLMs as operating systems" |
| }, |
| "2306.07863": { |
| "arxivId": "2306.07863", |
| "title": "Synapse: Trajectory-as-exemplar prompting with memory for computer control" |
| }, |
| "2210.03629": { |
| "arxivId": "2210.03629", |
| "title": "React: Synergizing reasoning and acting in language models" |
| }, |
| "2302.02676": { |
| "arxivId": "2302.02676", |
| "title": "Chain of hindsight aligns language models with feedback" |
| }, |
| "2305.20050": { |
| "arxivId": "2305.20050", |
| "title": "Let's verify step by step" |
| }, |
| "2305.11598": { |
| "arxivId": "2305.11598", |
| "title": "Introspective tips: Large language model for in-context decision making" |
| }, |
| "2308.07921": { |
| "arxivId": "2308.07921", |
| "title": "Solving challenging math word problems using GPT-4 code interpreter with code-based self-verification" |
| }, |
| "2210.03821": { |
| "arxivId": "2210.03821", |
| "title": "Large language models can implement policy iteration" |
| }, |
| "2206.02336": { |
| "arxivId": "2206.02336", |
| "title": "On the advance of making language models better reasoners" |
| }, |
| "2309.17382": { |
| "arxivId": "2309.17382", |
| "title": "Reason for future, act for now: A principled framework for autonomous LLM agents with provable sample efficiency" |
| }, |
| "1707.06347": { |
| "arxivId": "1707.06347", |
| "title": "Proximal policy optimization algorithms" |
| }, |
| "2303.17491": { |
| "arxivId": "2303.17491", |
| "title": "Language models can solve computer tasks" |
| }, |
| "2307.13854": { |
| "arxivId": "2307.13854", |
| "title": "Webarena: A realistic web environment for building autonomous agents" |
| }, |
| "2112.09332": { |
| "arxivId": "2112.09332", |
| "title": "Webgpt: Browser-assisted question-answering with human feedback" |
| }, |
| "2305.19308": { |
| "arxivId": "2305.19308", |
| "title": "SheetCopilot: Bringing software productivity to the next level through large language models" |
| }, |
| "2308.04026": { |
| "arxivId": "2308.04026", |
| "title": "AgentSims: An open-source sandbox for large language model evaluation" |
| }, |
| "2309.17234": { |
| "arxivId": "2309.17234", |
| "title": "LLM-Deliberation: Evaluating LLMs with Interactive Multi-Agent Negotiation Games" |
| }, |
| "2308.00245": { |
| "arxivId": "2308.00245", |
| "title": "The hitchhiker's guide to program analysis: A journey with large language models" |
| }, |
| "2307.01848": { |
| "arxivId": "2307.01848", |
| "title": "Embodied task planning with large language models" |
| }, |
| "2309.06719": { |
| "arxivId": "2309.06719", |
| "title": "TrafficGPT: Viewing, processing and interacting with traffic foundation models" |
| }, |
| "2310.05746": { |
| "arxivId": "2310.05746", |
| "title": "Put your money where your mouth is: Evaluating strategic planning and execution of LLM agents in an auction arena" |
| }, |
| "2205.00445": { |
| "arxivId": "2205.00445", |
| "title": "MRKL systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning" |
| }, |
| "2205.12255": { |
| "arxivId": "2205.12255", |
| "title": "TALM: Tool augmented language models" |
| }, |
| "2305.15334": { |
| "arxivId": "2305.15334", |
| "title": "Gorilla: Large language model connected with massive APIs" |
| }, |
| "2306.06624": { |
| "arxivId": "2306.06624", |
| "title": "RestGPT: Connecting large language models with real-world applications via RESTful APIs" |
| }, |
| "2308.05481": { |
| "arxivId": "2308.05481", |
| "title": "LLM as DBA" |
| }, |
| "2305.14323": { |
| "arxivId": "2305.14323", |
| "title": "ChatCoT: Tool-augmented chain-of-thought reasoning on chat-based large language models" |
| }, |
| "2308.04030": { |
| "arxivId": "2308.04030", |
| "title": "Gentopia: A collaborative platform for tool-augmented LLMs" |
| }, |
| "2305.17126": { |
| "arxivId": "2305.17126", |
| "title": "Large language models as tool makers" |
| }, |
| "2308.05960": { |
| "arxivId": "2308.05960", |
| "title": "BOLAA: Benchmarking and orchestrating LLM-augmented autonomous agents" |
| }, |
| "2308.12503": { |
| "arxivId": "2308.12503", |
| "title": "CGMI: configurable general multi-agent interaction framework" |
| }, |
| "2305.19118": { |
| "arxivId": "2305.19118", |
| "title": "Encouraging divergent thinking in large language models through multi-agent debate" |
| }, |
| "2307.02757": { |
| "arxivId": "2307.02757", |
| "title": "Wireless multi-agent generative AI: From connected intelligence to collective intelligence" |
| }, |
| "2308.01552": { |
| "arxivId": "2308.01552", |
| "title": "Interact: Exploring the potentials of chatgpt as a cooperative agent" |
| }, |
| "2010.03768": { |
| "arxivId": "2010.03768", |
| "title": "ALFWorld: Aligning text and embodied environments for interactive learning" |
| }, |
| "2310.00280": { |
| "arxivId": "2310.00280", |
| "title": "CoreX: Pushing the boundaries of complex reasoning through multi-model collaboration" |
| }, |
| "1910.05789": { |
| "arxivId": "1910.05789", |
| "title": "On the utility of learning about humans for human-ai coordination" |
| }, |
| "1906.00744": { |
| "arxivId": "1906.00744", |
| "title": "Hierarchical decision making by generating and following natural language instructions" |
| }, |
| "2308.03958": { |
| "arxivId": "2308.03958", |
| "title": "Simple synthetic data reduces sycophancy in large language models" |
| }, |
| "2309.05922": { |
| "arxivId": "2309.05922", |
| "title": "A survey of hallucination in large foundation models" |
| }, |
| "1809.09600": { |
| "arxivId": "1809.09600", |
| "title": "HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering" |
| }, |
| "2105.09938": { |
| "arxivId": "2105.09938", |
| "title": "Measuring coding challenge competence with APPS" |
| }, |
| "2207.01206": { |
| "arxivId": "2207.01206", |
| "title": "Webshop: Towards scalable real-world web interaction with grounded language agents" |
| }, |
| "1803.05355": { |
| "arxivId": "1803.05355", |
| "title": "FEVER: A large-scale dataset for fact extraction and verification" |
| }, |
| "2304.08354": { |
| "arxivId": "2304.08354", |
| "title": "Tool learning with foundation models" |
| }, |
| "1910.11215": { |
| "arxivId": "1910.11215", |
| "title": "RoboNet: Large-scale multi-robot learning" |
| }, |
| "2308.12952": { |
| "arxivId": "2308.12952", |
| "title": "BridgeData v2: A dataset for robot learning at scale" |
| }, |
| "2310.03128": { |
| "arxivId": "2310.03128", |
| "title": "MetaTool benchmark for large language models: Deciding whether to use tools and which to use" |
| }, |
| "2306.15626": { |
| "arxivId": "2306.15626", |
| "title": "LeanDojo: Theorem proving with retrieval-augmented language models" |
| }, |
| "2309.05689": { |
| "arxivId": "2309.05689", |
| "title": "Large language model for science: A study on P vs. NP" |
| }, |
| "2309.02726": { |
| "arxivId": "2309.02726", |
| "title": "Large language models for automated open-domain scientific hypotheses discovery" |
| }, |
| "2309.17452": { |
| "arxivId": "2309.17452", |
| "title": "TORA: A tool-integrated reasoning agent for mathematical problem solving" |
| }, |
| "1810.08678": { |
| "arxivId": "1810.08678", |
| "title": "Optimization of molecules via deep reinforcement learning" |
| }, |
| "1806.02473": { |
| "arxivId": "1806.02473", |
| "title": "Graph convolutional policy network for goal-directed molecular graph generation" |
| }, |
| "2310.04292": { |
| "arxivId": "2310.04292", |
| "title": "Towards foundational models for molecular learning on large-scale multi-task datasets" |
| }, |
| "2203.04115": { |
| "arxivId": "2203.04115", |
| "title": "Biological sequence design with GFlowNets" |
| }, |
| "2310.02031": { |
| "arxivId": "2310.02031", |
| "title": "OceanGPT: A large language model for ocean science tasks" |
| }, |
| "2304.00116": { |
| "arxivId": "2304.00116", |
| "title": "Enhancing large language models with climate resources" |
| }, |
| "2309.00986": { |
| "arxivId": "2309.00986", |
| "title": "ModelScope-Agent: Building your customizable agent system with open-source large language models" |
| }, |
| "2305.12487": { |
| "arxivId": "2305.12487", |
| "title": "Augmenting autotelic agents with large language models" |
| }, |
| "2305.15393": { |
| "arxivId": "2305.15393", |
| "title": "LayoutGPT: Compositional visual planning and generation with large language models" |
| }, |
| "2310.05029": { |
| "arxivId": "2310.05029", |
| "title": "Walking down the memory maze: Beyond context limit through interactive reading" |
| }, |
| "2205.06760": { |
| "arxivId": "2205.06760", |
| "title": "Emergent bartering behaviour in multi-agent reinforcement learning" |
| }, |
| "2004.13332": { |
| "arxivId": "2004.13332", |
| "title": "The AI Economist: Improving equality and productivity with AI-driven tax policies" |
| }, |
| "2308.00016": { |
| "arxivId": "2308.00016", |
| "title": "Alpha-GPT: Human-AI interactive alpha mining for quantitative investment" |
| }, |
| "2108.02904": { |
| "arxivId": "2108.02904", |
| "title": "Building a foundation for data-driven, interpretable, and robust policy design using the AI Economist" |
| }, |
| "2305.16960": { |
| "arxivId": "2305.16960", |
| "title": "Training socially aligned language models in simulated human society" |
| }, |
| "2304.13835": { |
| "arxivId": "2304.13835", |
| "title": "Multi-party chat: Conversational agents in group settings with humans and models" |
| }, |
| "2308.15197": { |
| "arxivId": "2308.15197", |
| "title": "Where would I go next? Large language models as human mobility predictors" |
| }, |
| "2310.11761": { |
| "arxivId": "2310.11761", |
| "title": "A comprehensive evaluation of large language models on legal judgment prediction" |
| }, |
| "2310.05418": { |
| "arxivId": "2310.05418", |
| "title": "Humanoid agents: Platform for simulating human-like generative agents" |
| }, |
| "2305.20076": { |
| "arxivId": "2305.20076", |
| "title": "Decision-oriented dialogue for human-ai collaboration" |
| }, |
| "2307.12856": { |
| "arxivId": "2307.12856", |
| "title": "A real-world webagent with planning, long context understanding, and program synthesis" |
| }, |
| "2305.03403": { |
| "arxivId": "2305.03403", |
| "title": "LLMs for semi-automated data science: Introducing CAAFE for context-aware automated feature engineering" |
| }, |
| "2306.05152": { |
| "arxivId": "2306.05152", |
| "title": "Towards autonomous testing agents via conversational large language models" |
| }, |
| "2310.16340": { |
| "arxivId": "2310.16340", |
| "title": "RCAgent: Cloud root cause analysis by autonomous agents with tool-augmented large language models" |
| }, |
| "2306.02552": { |
| "arxivId": "2306.02552", |
| "title": "RecAgent: A novel simulation paradigm for recommender systems" |
| }, |
| "2305.13455": { |
| "arxivId": "2305.13455", |
| "title": "CLEMBench: Using game play to evaluate chat-optimized language models as conversational agents" |
| }, |
| "2310.08067": { |
| "arxivId": "2310.08067", |
| "title": "GameGPT: Multi-agent collaborative framework for game development" |
| }, |
| "2209.11302": { |
| "arxivId": "2209.11302", |
| "title": "ProgPrompt: Generating situated robot task plans using large language models" |
| }, |
| "2207.05608": { |
| "arxivId": "2207.05608", |
| "title": "Inner monologue: Embodied reasoning through planning with language models" |
| }, |
| "2305.10626": { |
| "arxivId": "2305.10626", |
| "title": "Language models meet world models: Embodied experiences enhance language models" |
| }, |
| "2307.12981": { |
| "arxivId": "2307.12981", |
| "title": "3D-LLM: Injecting the 3D world into large language models" |
| }, |
| "1903.04527": { |
| "arxivId": "1903.04527", |
| "title": "Multi-agent deep reinforcement learning for large-scale traffic signal control" |
| }, |
| "2309.16292": { |
| "arxivId": "2309.16292", |
| "title": "Dilu: A knowledge-driven approach to autonomous driving with large language models" |
| }, |
| "2308.10435": { |
| "arxivId": "2308.10435", |
| "title": "GPT-in-the-loop: Adaptive decision-making for multiagent systems" |
| }, |
| "2308.03688": { |
| "arxivId": "2308.03688", |
| "title": "AgentBench: Evaluating LLMs as agents" |
| }, |
| "2308.03656": { |
| "arxivId": "2308.03656", |
| "title": "Emotionally numb or empathetic? Evaluating how LLMs feel using EmotionBench" |
| }, |
| "2305.14985": { |
| "arxivId": "2305.14985", |
| "title": "IdealGPT: Iteratively decomposing vision and language reasoning via large language models" |
| }, |
| "2309.17421": { |
| "arxivId": "2309.17421", |
| "title": "The dawn of LMMs: Preliminary explorations with GPT-4V(ision)" |
| }, |
| "2310.09478": { |
| "arxivId": "2310.09478", |
| "title": "MiniGPT-v2: Large language model as a unified interface for vision-language multi-task learning" |
| }, |
| "2304.08485": { |
| "arxivId": "2304.08485", |
| "title": "Visual instruction tuning" |
| }, |
| "2307.03172": { |
| "arxivId": "2307.03172", |
| "title": "Lost in the Middle: How Language Models Use Long Contexts" |
| }, |
| "2309.01219": { |
| "arxivId": "2309.01219", |
| "title": "Siren's song in the AI ocean: A survey on hallucination in large language models" |
| }, |
| "2310.03094": { |
| "arxivId": "2310.03094", |
| "title": "Large language model cascades with mixture of thoughts representations for cost-efficient reasoning" |
| }, |
| "2308.05391": { |
| "arxivId": "2308.05391", |
| "title": "Enhancing trust in LLM-based AI automation agents: New considerations and future challenges" |
| }, |
| "2309.15817": { |
| "arxivId": "2309.15817", |
| "title": "Identifying the risks of LM agents with an LM-emulated sandbox" |
| }, |
| "2305.17144": { |
| "arxivId": "2305.17144", |
| "title": "Ghost in the Minecraft: Generally capable agents for open-world environments via large language models with text-based knowledge and memory" |
| }, |
| "2305.02412": { |
| "arxivId": "2305.02412", |
| "title": "Plan, eliminate, and track - language models are good teachers for embodied agents" |
| }, |
| "2306.08129": { |
| "arxivId": "2306.08129", |
| "title": "AVIS: Autonomous Visual Information Seeking with Large Language Models" |
| }, |
| "2310.04353": { |
| "arxivId": "2310.04353", |
| "title": "An In-Context Learning Agent for Formal Theorem-Proving" |
| }, |
| "2309.17277": { |
| "arxivId": "2309.17277", |
| "title": "Suspicion-Agent: Playing Imperfect Information Games with Theory of Mind Aware GPT-4" |
| }, |
| "2308.14284": { |
| "arxivId": "2308.14284", |
| "title": "Prompt to Transfer: Sim-to-Real Transfer for Traffic Signal Control with Prompt Learning" |
| }, |
| "2305.13246": { |
| "arxivId": "2305.13246", |
| "title": "Interactive natural language processing" |
| }, |
| "2203.02155": { |
| "arxivId": "2203.02155", |
| "title": "Training language models to follow instructions with human feedback" |
| }, |
| "2206.07682": { |
| "arxivId": "2206.07682", |
| "title": "Emergent abilities of large language models" |
| }, |
| "2004.10151": { |
| "arxivId": "2004.10151", |
| "title": "Experience grounds language" |
| }, |
| "2005.14165": { |
| "arxivId": "2005.14165", |
| "title": "Language models are few-shot learners" |
| }, |
| "2010.11939": { |
| "arxivId": "2010.11939", |
| "title": "Limitations of autoregressive models and their alternatives" |
| }, |
| "2212.01681": { |
| "arxivId": "2212.01681", |
| "title": "Language models as agent models" |
| }, |
| "2306.12672": { |
| "arxivId": "2306.12672", |
| "title": "From word models to world models: Translating from natural language to the probabilistic language of thought" |
| }, |
| "1704.01444": { |
| "arxivId": "1704.01444", |
| "title": "Learning to generate reviews and discovering sentiment" |
| }, |
| "2106.00737": { |
| "arxivId": "2106.00737", |
| "title": "Implicit representations of meaning in neural language models" |
| }, |
| "1701.07274": { |
| "arxivId": "1701.07274", |
| "title": "Deep reinforcement learning: An overview" |
| }, |
| "1312.5602": { |
| "arxivId": "1312.5602", |
| "title": "Playing atari with deep reinforcement learning" |
| }, |
| "1810.00123": { |
| "arxivId": "1810.00123", |
| "title": "Generalization and regularization in DQN" |
| }, |
| "1804.06893": { |
| "arxivId": "1804.06893", |
| "title": "A study on overfitting in deep reinforcement learning" |
| }, |
| "1806.10729": { |
| "arxivId": "1806.10729", |
| "title": "Illuminating generalization in deep reinforcement learning through procedural level generation" |
| }, |
| "2107.06277": { |
| "arxivId": "2107.06277", |
| "title": "Why generalization in RL is difficult: Epistemic pomdps and implicit partial observability" |
| }, |
| "1511.06342": { |
| "arxivId": "1511.06342", |
| "title": "Actor-mimic: Deep multitask and transfer reinforcement learning" |
| }, |
| "2009.07888": { |
| "arxivId": "2009.07888", |
| "title": "Transfer learning in deep reinforcement learning: A survey" |
| }, |
| "1703.03400": { |
| "arxivId": "1703.03400", |
| "title": "Model-agnostic meta-learning for fast adaptation of deep networks" |
| }, |
| "1802.07245": { |
| "arxivId": "1802.07245", |
| "title": "Meta-reinforcement learning of structured exploration strategies" |
| }, |
| "1903.08254": { |
| "arxivId": "1903.08254", |
| "title": "Efficient off-policy meta-reinforcement learning via probabilistic context variables" |
| }, |
| "1910.00125": { |
| "arxivId": "1910.00125", |
| "title": "Meta-q-learning" |
| }, |
| "1810.03548": { |
| "arxivId": "1810.03548", |
| "title": "Meta-learning: A survey" |
| }, |
| "1805.10886": { |
| "arxivId": "1805.10886", |
| "title": "Importance weighted transfer of samples in reinforcement learning" |
| }, |
| "2301.08028": { |
| "arxivId": "2301.08028", |
| "title": "A survey of meta-reinforcement learning" |
| }, |
| "2305.14497": { |
| "arxivId": "2305.14497", |
| "title": "Self-polish: Enhance reasoning in large language models via problem refinement" |
| }, |
| "2305.08844": { |
| "arxivId": "2305.08844", |
| "title": "RL4F: generating natural language feedback with reinforcement learning for repairing model outputs" |
| }, |
| "2302.12813": { |
| "arxivId": "2302.12813", |
| "title": "Check your facts and try again: Improving large language models with external knowledge and automated feedback" |
| }, |
| "2109.01652": { |
| "arxivId": "2109.01652", |
| "title": "Finetuned language models are zero-shot learners" |
| }, |
| "2110.08207": { |
| "arxivId": "2110.08207", |
| "title": "Multitask prompted training enables zero-shot task generalization" |
| }, |
| "2210.11416": { |
| "arxivId": "2210.11416", |
| "title": "Scaling instruction-finetuned language models" |
| }, |
| "2304.00008": { |
| "arxivId": "2304.00008", |
| "title": "On the creativity of large language models" |
| }, |
| "2304.10592": { |
| "arxivId": "2304.10592", |
| "title": "Minigpt-4: Enhancing vision-language understanding with advanced large language models" |
| }, |
| "2306.13549": { |
| "arxivId": "2306.13549", |
| "title": "A survey on multimodal large language models" |
| }, |
| "2305.15021": { |
| "arxivId": "2305.15021", |
| "title": "EmbodiedGPT: Vision-language pre-training via embodied chain of thought" |
| }, |
| "2302.06706": { |
| "arxivId": "2302.06706", |
| "title": "On the planning abilities of large language models (A critical investigation with a proposed benchmark)" |
| }, |
| "2305.13711": { |
| "arxivId": "2305.13711", |
| "title": "LLM-eval: Unified multi-dimensional automatic evaluation for open-domain conversations with large language models" |
| }, |
| "2305.10142": { |
| "arxivId": "2305.10142", |
| "title": "Improving language model negotiation with self-play and in-context learning from AI feedback" |
| }, |
| "2304.01746": { |
| "arxivId": "2304.01746", |
| "title": "Is chatgpt a highly fluent grammatical error correction system? A comprehensive evaluation" |
| }, |
| "2302.09185": { |
| "arxivId": "2302.09185", |
| "title": "Bounding the capabilities of large language models in open text generation with prompt constraints" |
| }, |
| "2305.14763": { |
| "arxivId": "2305.14763", |
| "title": "Clever hans or neural theory of mind? stress testing social reasoning in large language models" |
| }, |
| "1602.03483": { |
| "arxivId": "1602.03483", |
| "title": "Learning distributed representations of sentences from unlabelled data" |
| }, |
| "1103.0398": { |
| "arxivId": "1103.0398", |
| "title": "Natural language processing (almost) from scratch" |
| }, |
| "2001.08361": { |
| "arxivId": "2001.08361", |
| "title": "Scaling laws for neural language models" |
| }, |
| "2002.08910": { |
| "arxivId": "2002.08910", |
| "title": "How much knowledge can you pack into the parameters of a language model?" |
| }, |
| "2010.05731": { |
| "arxivId": "2010.05731", |
| "title": "Probing pretrained language models for lexical semantics" |
| }, |
| "2110.04984": { |
| "arxivId": "2110.04984", |
| "title": "Advances in multi-turn dialogue comprehension: A survey" |
| }, |
| "2104.05837": { |
| "arxivId": "2104.05837", |
| "title": "Relational world knowledge representation in contextual language models: A review" |
| }, |
| "2211.11501": { |
| "arxivId": "2211.11501", |
| "title": "DS-1000: A natural and reliable benchmark for data science code generation" |
| }, |
| "2204.06031": { |
| "arxivId": "2204.06031", |
| "title": "A review on language models as knowledge bases" |
| }, |
| "1708.02072": { |
| "arxivId": "1708.02072", |
| "title": "Measuring catastrophic forgetting in neural networks" |
| }, |
| "2104.08164": { |
| "arxivId": "2104.08164", |
| "title": "Editing factual knowledge in language models" |
| }, |
| "2206.06520": { |
| "arxivId": "2206.06520", |
| "title": "Memory-based model editing at scale" |
| }, |
| "2303.08896": { |
| "arxivId": "2303.08896", |
| "title": "SelfCheckGPT: Zero-resource black-box hallucination detection for generative large language models" |
| }, |
| "2305.14623": { |
| "arxivId": "2305.14623", |
| "title": "Self-checker: Plug-and-play modules for fact-checking with large language models" |
| }, |
| "2305.11738": { |
| "arxivId": "2305.11738", |
| "title": "CRITIC: large language models can self-correct with tool-interactive critiquing" |
| }, |
| "1910.13461": { |
| "arxivId": "1910.13461", |
| "title": "BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension" |
| }, |
| "2203.11258": { |
| "arxivId": "2203.11258", |
| "title": "Efficient classification of long documents using transformers" |
| }, |
| "2304.13343": { |
| "arxivId": "2304.13343", |
| "title": "Unleashing infinite-length input capacity for large-scale language models with self-controlled memory system" |
| }, |
| "2305.14322": { |
| "arxivId": "2305.14322", |
| "title": "RET-LLM: towards a general read-write memory for large language models" |
| }, |
| "2306.03901": { |
| "arxivId": "2306.03901", |
| "title": "ChatDB: Augmenting LLMs with databases as their symbolic memory" |
| }, |
| "2205.09712": { |
| "arxivId": "2205.09712", |
| "title": "Selection-inference: Exploiting large language models for interpretable logical reasoning" |
| }, |
| "2110.01691": { |
| "arxivId": "2110.01691", |
| "title": "AI chains: Transparent and controllable human-ai interaction by chaining large language model prompts" |
| }, |
| "2303.08268": { |
| "arxivId": "2303.08268", |
| "title": "Chat with the environment: Interactive multimodal perception using large language models" |
| }, |
| "2308.00436": { |
| "arxivId": "2308.00436", |
| "title": "SelfCheck: Using LLMs to zero-shot check their own step-by-step reasoning" |
| }, |
| "2212.02499": { |
| "arxivId": "2212.02499", |
| "title": "Images speak in images: A generalist painter for in-context visual learning" |
| }, |
| "2301.02111": { |
| "arxivId": "2301.02111", |
| "title": "Neural codec language models are zero-shot text to speech synthesizers" |
| }, |
| "2211.12701": { |
| "arxivId": "2211.12701", |
| "title": "Continual learning of natural language processing tasks: A survey" |
| }, |
| "2302.00487": { |
| "arxivId": "2302.00487", |
| "title": "A comprehensive survey of continual learning: Theory, method and application" |
| }, |
| "2301.12314": { |
| "arxivId": "2301.12314", |
| "title": "Progressive prompts: Continual learning for language models" |
| }, |
| "2302.13971": { |
| "arxivId": "2302.13971", |
| "title": "LLaMA: Open and efficient foundation language models" |
| }, |
| "2211.05100": { |
| "arxivId": "2211.05100", |
| "title": "BLOOM: A 176B-parameter open-access multilingual language model" |
| }, |
| "1611.06216": { |
| "arxivId": "1611.06216", |
| "title": "Generative deep neural networks for dialogue: A short review" |
| }, |
| "1506.05869": { |
| "arxivId": "1506.05869", |
| "title": "A neural conversational model" |
| }, |
| "2001.09977": { |
| "arxivId": "2001.09977", |
| "title": "Towards a human-like open-domain chatbot" |
| }, |
| "2004.13637": { |
| "arxivId": "2004.13637", |
| "title": "Recipes for building an open-domain chatbot" |
| }, |
| "1910.10683": { |
| "arxivId": "1910.10683", |
| "title": "Exploring the limits of transfer learning with a unified text-to-text transformer" |
| }, |
| "2304.04370": { |
| "arxivId": "2304.04370", |
| "title": "OpenAGI: When LLM meets domain experts" |
| }, |
| "1606.05250": { |
| "arxivId": "1606.05250", |
| "title": "SQuAD: 100,000+ questions for machine comprehension of text" |
| }, |
| "2303.12528": { |
| "arxivId": "2303.12528", |
| "title": "MEGA: Multilingual evaluation of generative AI" |
| }, |
| "1909.10705": { |
| "arxivId": "1909.10705", |
| "title": "Do massively pretrained language models make better storytellers?" |
| }, |
| "2111.09509": { |
| "arxivId": "2111.09509", |
| "title": "How much do language models copy from their training data? Evaluating linguistic novelty in text generation using RAVEN" |
| }, |
| "2009.14715": { |
| "arxivId": "2009.14715", |
| "title": "Learning rewards from linguistic feedback" |
| }, |
| "2002.04833": { |
| "arxivId": "2002.04833", |
| "title": "Reward-rational (implicit) choice: A unifying formalism for reward learning" |
| }, |
| "2302.00093": { |
| "arxivId": "2302.00093", |
| "title": "Large language models can be easily distracted by irrelevant context" |
| }, |
| "2302.07842": { |
| "arxivId": "2302.07842", |
| "title": "Augmented language models: a survey" |
| }, |
| "2307.11019": { |
| "arxivId": "2307.11019", |
| "title": "Investigating the factual knowledge boundary of large language models with retrieval augmentation" |
| }, |
| "cs/0004001": { |
| "arxivId": "cs/0004001", |
| "title": "A theory of universal artificial intelligence based on algorithmic complexity" |
| }, |
| "1905.06566": { |
| "arxivId": "1905.06566", |
| "title": "HIBERT: Document level pre-training of hierarchical bidirectional transformers for document summarization" |
| }, |
| "2305.16300": { |
| "arxivId": "2305.16300", |
| "title": "Landmark attention: Random-access infinite context length for transformers" |
| }, |
| "2210.05529": { |
| "arxivId": "2210.05529", |
| "title": "An exploration of hierarchical attention transformers for efficient long document classification" |
| }, |
| "2305.01625": { |
| "arxivId": "2305.01625", |
| "title": "Unlimiformer: Long-range transformers with unlimited length input" |
| }, |
| "2007.14062": { |
| "arxivId": "2007.14062", |
| "title": "Big Bird: Transformers for longer sequences" |
| }, |
| "2212.09196": { |
| "arxivId": "2212.09196", |
| "title": "Emergent analogical reasoning in large language models" |
| }, |
| "2305.15408": { |
| "arxivId": "2305.15408", |
| "title": "Towards revealing the mystery behind chain of thought: a theoretical perspective" |
| }, |
| "2305.18323": { |
| "arxivId": "2305.18323", |
| "title": "ReWOO: Decoupling reasoning from observations for efficient augmented language models" |
| }, |
| "2307.06135": { |
| "arxivId": "2307.06135", |
| "title": "SayPlan: Grounding large language models using 3D scene graphs for scalable task planning" |
| }, |
| "1802.05365": { |
| "arxivId": "1802.05365", |
| "title": "Deep contextualized word representations" |
| }, |
| "2106.10328": { |
| "arxivId": "2106.10328", |
| "title": "Process for adapting language models to society (PALMS) with values-targeted datasets" |
| }, |
| "2202.01279": { |
| "arxivId": "2202.01279", |
| "title": "PromptSource: An integrated development environment and repository for natural language prompts" |
| }, |
| "2212.12017": { |
| "arxivId": "2212.12017", |
| "title": "OPT-IML: Scaling language model instruction meta learning through the lens of generalization" |
| }, |
| "2106.13884": { |
| "arxivId": "2106.13884", |
| "title": "Multimodal few-shot learning with frozen language models" |
| }, |
| "2209.00647": { |
| "arxivId": "2209.00647", |
| "title": "Visual prompting via image inpainting" |
| }, |
| "2304.04675": { |
| "arxivId": "2304.04675", |
| "title": "Multilingual machine translation with large language models: Empirical results and analysis" |
| }, |
| "2303.03926": { |
| "arxivId": "2303.03926", |
| "title": "Speak foreign languages with your own voice: Cross-lingual neural codec language modeling" |
| }, |
| "1606.09282": { |
| "arxivId": "1606.09282", |
| "title": "Learning without forgetting" |
| }, |
| "1910.07104": { |
| "arxivId": "1910.07104", |
| "title": "Orthogonal gradient descent for continual learning" |
| }, |
| "2304.06027": { |
| "arxivId": "2304.06027", |
| "title": "Continual diffusion: Continual customization of text-to-image diffusion with C-LoRA" |
| }, |
| "1706.08840": { |
| "arxivId": "1706.08840", |
| "title": "Gradient episodic memory for continual learning" |
| }, |
| "1906.01076": { |
| "arxivId": "1906.01076", |
| "title": "Episodic memory in lifelong language learning" |
| }, |
| "1811.11682": { |
| "arxivId": "1811.11682", |
| "title": "Experience replay for continual learning" |
| }, |
| "1801.01423": { |
| "arxivId": "1801.01423", |
| "title": "Overcoming catastrophic forgetting with hard attention to the task" |
| }, |
| "2010.11929": { |
| "arxivId": "2010.11929", |
| "title": "An image is worth 16x16 words: Transformers for image recognition at scale" |
| }, |
| "1711.00937": { |
| "arxivId": "1711.00937", |
| "title": "Neural discrete representation learning" |
| }, |
| "2110.02178": { |
| "arxivId": "2110.02178", |
| "title": "MobileViT: Light-weight, general-purpose, and mobile-friendly vision transformer" |
| }, |
| "2105.01601": { |
| "arxivId": "2105.01601", |
| "title": "MLP-Mixer: An all-MLP architecture for vision" |
| }, |
| "2302.14045": { |
| "arxivId": "2302.14045", |
| "title": "Language is not all you need: Aligning perception with language models" |
| }, |
| "2305.04790": { |
| "arxivId": "2305.04790", |
| "title": "Multimodal-GPT: A vision and language model for dialogue with humans" |
| }, |
| "2204.14198": { |
| "arxivId": "2204.14198", |
| "title": "Flamingo: a visual language model for few-shot learning" |
| }, |
| "2305.16355": { |
| "arxivId": "2305.16355", |
| "title": "PandaGPT: One model to instruction-follow them all" |
| }, |
| "2104.01778": { |
| "arxivId": "2104.01778", |
| "title": "AST: Audio spectrogram transformer" |
| }, |
| "2106.07447": { |
| "arxivId": "2106.07447", |
| "title": "HuBERT: Self-supervised speech representation learning by masked prediction of hidden units" |
| }, |
| "2305.04160": { |
| "arxivId": "2305.04160", |
| "title": "X-LLM: Bootstrapping advanced large language models by treating multi-modalities as foreign languages" |
| }, |
| "2305.05662": { |
| "arxivId": "2305.05662", |
| "title": "InternGPT: Solving vision-centric tasks by interacting with chatbots beyond language" |
| }, |
| "1908.06954": { |
| "arxivId": "1908.06954", |
| "title": "Attention on attention for image captioning" |
| }, |
| "2003.14080": { |
| "arxivId": "2003.14080", |
| "title": "X-linear attention networks for image captioning" |
| }, |
| "2305.06355": { |
| "arxivId": "2305.06355", |
| "title": "VideoChat: Chat-centric video understanding" |
| }, |
| "2308.01399": { |
| "arxivId": "2308.01399", |
| "title": "Learning to model the world with language" |
| }, |
| "2012.12877": { |
| "arxivId": "2012.12877", |
| "title": "Training data-efficient image transformers & distillation through attention" |
| }, |
| "2206.08916": { |
| "arxivId": "2206.08916", |
| "title": "UNIFIED-IO: A unified model for vision, language, and multi-modal tasks" |
| }, |
| "2306.14824": { |
| "arxivId": "2306.14824", |
| "title": "Kosmos-2: Grounding multimodal large language models to the world" |
| }, |
| "2306.09093": { |
| "arxivId": "2306.09093", |
| "title": "Macaw-LLM: Multi-modal language modeling with image, audio, video, and text integration" |
| }, |
| "2306.05424": { |
| "arxivId": "2306.05424", |
| "title": "Video-ChatGPT: Towards detailed video understanding via large vision and language models" |
| }, |
| "2304.03373": { |
| "arxivId": "2304.03373", |
| "title": "Training-free layout control with cross-attention guidance" |
| }, |
| "2212.04356": { |
| "arxivId": "2212.04356", |
| "title": "Robust speech recognition via large-scale weak supervision" |
| }, |
| "2204.11792": { |
| "arxivId": "2204.11792", |
| "title": "SyntaSpeech: Syntax-aware generative adversarial text-to-speech" |
| }, |
| "2106.06103": { |
| "arxivId": "2106.06103", |
| "title": "Conditional variational autoencoder with adversarial learning for end-to-end text-to-speech" |
| }, |
| "2211.12433": { |
| "arxivId": "2211.12433", |
| "title": "TF-GridNet: Integrating full- and sub-band modeling for speech separation" |
| }, |
| "2105.02446": { |
| "arxivId": "2105.02446", |
| "title": "DiffSinger: Singing voice synthesis via shallow diffusion mechanism" |
| }, |
| "2109.12804": { |
| "arxivId": "2109.12804", |
| "title": "Fast-MD: Fast multi-decoder end-to-end speech translation with non-autoregressive hidden intermediates" |
| }, |
| "2304.05128": { |
| "arxivId": "2304.05128", |
| "title": "Teaching large language models to self-debug" |
| }, |
| "2210.13431": { |
| "arxivId": "2210.13431", |
| "title": "Instruction-following agents with jointly pre-trained vision-language models" |
| }, |
| "2210.06407": { |
| "arxivId": "2210.06407", |
| "title": "Interactive language: Talking to robots in real time" |
| }, |
| "2305.18898": { |
| "arxivId": "2305.18898", |
| "title": "AlphaBlock: Embodied finetuning for vision-language reasoning in robot manipulation" |
| }, |
| "2207.04429": { |
| "arxivId": "2207.04429", |
| "title": "LM-Nav: Robotic navigation with large pre-trained models of language, vision, and action" |
| }, |
| "2305.16986": { |
| "arxivId": "2305.16986", |
| "title": "NavGPT: Explicit reasoning in vision-and-language navigation with large language models" |
| }, |
| "2206.08853": { |
| "arxivId": "2206.08853", |
| "title": "MineDojo: Building open-ended embodied agents with internet-scale knowledge" |
| }, |
| "2106.14876": { |
| "arxivId": "2106.14876", |
| "title": "Multi-task curriculum learning in a complex, visual, hard-exploration domain: Minecraft" |
| }, |
| "2301.12507": { |
| "arxivId": "2301.12507", |
| "title": "Distilling internet-scale vision-language models into embodied agents" |
| }, |
| "2301.13188": { |
| "arxivId": "2301.13188", |
| "title": "Extracting training data from diffusion models" |
| }, |
| "2305.18703": { |
| "arxivId": "2305.18703", |
| "title": "Domain specialization as the key to make large language models disruptive: A comprehensive survey" |
| }, |
| "2307.15043": { |
| "arxivId": "2307.15043", |
| "title": "Universal and transferable adversarial attacks on aligned language models" |
| }, |
| "1707.03374": { |
| "arxivId": "1707.03374", |
| "title": "Imitation from observation: Learning to imitate behaviors from raw video via context translation" |
| }, |
| "2206.11795": { |
| "arxivId": "2206.11795", |
| "title": "Video pretraining (VPT): Learning to act by watching unlabeled online videos" |
| }, |
| "2307.04964": { |
| "arxivId": "2307.04964", |
| "title": "Secrets of RLHF in large language models part I: PPO" |
| }, |
| "2306.08302": { |
| "arxivId": "2306.08302", |
| "title": "Unifying large language models and knowledge graphs: A roadmap" |
| }, |
| "2103.04918": { |
| "arxivId": "2103.04918", |
| "title": "A survey of embodied AI: from simulators to research tasks" |
| }, |
| "1806.10293": { |
| "arxivId": "1806.10293", |
| "title": "Qt-opt: Scalable deep reinforcement learning for vision-based robotic manipulation" |
| }, |
| "1806.07011": { |
| "arxivId": "1806.07011", |
| "title": "Virtualhome: Simulating household activities via programs" |
| }, |
| "2108.04927": { |
| "arxivId": "2108.04927", |
| "title": "Embodied BERT: A transformer model for embodied, language-guided visual task completion" |
| }, |
| "1911.05892": { |
| "arxivId": "1911.05892", |
| "title": "Reinforcement learning for market making in a multi-agent dealer market" |
| }, |
| "1904.01201": { |
| "arxivId": "1904.01201", |
| "title": "Habitat: A platform for embodied AI research" |
| }, |
| "2301.13688": { |
| "arxivId": "2301.13688", |
| "title": "The flan collection: Designing data and methods for effective instruction tuning" |
| }, |
| "2209.07753": { |
| "arxivId": "2209.07753", |
| "title": "Code as policies: Language model programs for embodied control" |
| }, |
| "1910.11432": { |
| "arxivId": "1910.11432", |
| "title": "HRL4IN: hierarchical reinforcement learning for interactive navigation with mobile manipulators" |
| }, |
| "2210.07940": { |
| "arxivId": "2210.07940", |
| "title": "AVLEN: audio-visual-language embodied navigation in 3d environments" |
| }, |
| "2306.03604": { |
| "arxivId": "2306.03604", |
| "title": "Enabling intelligent interactions between an agent and an LLM: A reinforcement learning approach" |
| }, |
| "2012.09812": { |
| "arxivId": "2012.09812", |
| "title": "Ving: Learning open-world navigation with visual goals" |
| }, |
| "2210.05714": { |
| "arxivId": "2210.05714", |
| "title": "Visual language maps for robot navigation" |
| }, |
| "2203.05137": { |
| "arxivId": "2203.05137", |
| "title": "Cross-modal map learning for vision and language navigation" |
| }, |
| "2112.03857": { |
| "arxivId": "2112.03857", |
| "title": "Grounded language-image pre-training" |
| }, |
| "1912.11684": { |
| "arxivId": "1912.11684", |
| "title": "Look, listen, and act: Towards audio-visual embodied navigation" |
| }, |
| "2212.06817": { |
| "arxivId": "2212.06817", |
| "title": "RT-1: robotics transformer for real-world control at scale" |
| }, |
| "2307.15818": { |
| "arxivId": "2307.15818", |
| "title": "RT-2: vision-language-action models transfer web knowledge to robotic control" |
| }, |
| "2306.06070": { |
| "arxivId": "2306.06070", |
| "title": "Mind2web: Towards a generalist agent for the web" |
| }, |
| "2305.11854": { |
| "arxivId": "2305.11854", |
| "title": "Multimodal web navigation with instruction-finetuned foundation models" |
| }, |
| "2210.04964": { |
| "arxivId": "2210.04964", |
| "title": "Generating executable action plans with environmentally-aware language models" |
| }, |
| "2303.16563": { |
| "arxivId": "2303.16563", |
| "title": "Plan4mc: Skill reinforcement learning and planning for open-world minecraft tasks" |
| }, |
| "2304.12998": { |
| "arxivId": "2304.12998", |
| "title": "ChatLLM network: More brains, more intelligence" |
| }, |
| "2303.17071": { |
| "arxivId": "2303.17071", |
| "title": "DERA: enhancing large language model completions with dialog-enabled resolving agents" |
| }, |
| "2306.03314": { |
| "arxivId": "2306.03314", |
| "title": "Multi-agent collaboration: Harnessing the power of intelligent LLM agents" |
| }, |
| "2305.15075": { |
| "arxivId": "2305.15075", |
| "title": "HuatuoGPT, towards taming language model to be a doctor" |
| }, |
| "2308.03549": { |
| "arxivId": "2308.03549", |
| "title": "ZhongJing: Enhancing the chinese medical capabilities of large language model through expert feedback and real-world multi-turn dialogue" |
| }, |
| "2208.11663": { |
| "arxivId": "2208.11663", |
| "title": "PEER: A collaborative language model" |
| }, |
| "2210.05492": { |
| "arxivId": "2210.05492", |
| "title": "Mastering the game of no-press diplomacy via human-regularized reinforcement learning and planning" |
| }, |
| "2012.02757": { |
| "arxivId": "2012.02757", |
| "title": "Playing text-based games with common sense" |
| }, |
| "2107.08408": { |
| "arxivId": "2107.08408", |
| "title": "Pre-trained language models as prior knowledge for playing text-based games" |
| }, |
| "2010.00685": { |
| "arxivId": "2010.00685", |
| "title": "How to motivate your dragon: Teaching goal-driven agents to speak and act in fantasy worlds" |
| }, |
| "2103.16057": { |
| "arxivId": "2103.16057", |
| "title": "Grounding open-domain instructions to automate web support tasks" |
| }, |
| "2007.02701": { |
| "arxivId": "2007.02701", |
| "title": "Scaling imitation learning in minecraft" |
| }, |
| "2011.00583": { |
| "arxivId": "2011.00583", |
| "title": "An overview of multi-agent reinforcement learning from game theoretical perspective" |
| }, |
| "2305.13657": { |
| "arxivId": "2305.13657", |
| "title": "ChatGPT as your personal data scientist" |
| }, |
| "1706.05125": { |
| "arxivId": "1706.05125", |
| "title": "Deal or no deal? end-to-end learning of negotiation dialogues" |
| }, |
| "2103.14659": { |
| "arxivId": "2103.14659", |
| "title": "Alignment of language agents" |
| }, |
| "2209.00626": { |
| "arxivId": "2209.00626", |
| "title": "The alignment problem from a deep learning perspective" |
| }, |
| "1706.07230": { |
| "arxivId": "1706.07230", |
| "title": "Gated-attention architectures for task-oriented language grounding" |
| }, |
| "1611.09823": { |
| "arxivId": "1611.09823", |
| "title": "Dialogue learning with human-in-the-loop" |
| }, |
| "1704.08760": { |
| "arxivId": "1704.08760", |
| "title": "Learning a neural semantic parser from user feedback" |
| }, |
| "1604.06045": { |
| "arxivId": "1604.06045", |
| "title": "Dialog-based language learning" |
| }, |
| "2208.03188": { |
| "arxivId": "2208.03188", |
| "title": "Blenderbot 3: a deployed conversational agent that continually learns to responsibly engage" |
| }, |
| "2204.03685": { |
| "arxivId": "2204.03685", |
| "title": "Read, revise, repeat: A system demonstration for human-in-the-loop iterative text revision" |
| }, |
| "1804.05958": { |
| "arxivId": "1804.05958", |
| "title": "Can neural machine translation be improved with user feedback?" |
| }, |
| "1910.05389": { |
| "arxivId": "1910.05389", |
| "title": "Model-based interactive semantic parsing: A unified framework and A text-to-sql case study" |
| }, |
| "1905.04655": { |
| "arxivId": "1905.04655", |
| "title": "Improving natural language interaction with robots using advice" |
| }, |
| "2103.14540": { |
| "arxivId": "2103.14540", |
| "title": "NL-EDIT: correcting semantic parse errors through natural language interaction" |
| }, |
| "2112.09737": { |
| "arxivId": "2112.09737", |
| "title": "Learning to repair: Repairing model output errors after deployment using a dynamic memory of feedback" |
| }, |
| "2303.16755": { |
| "arxivId": "2303.16755", |
| "title": "Training language models with language feedback at scale" |
| }, |
| "2208.03270": { |
| "arxivId": "2208.03270", |
| "title": "Learning new skills after deployment: Improving open-domain internet-driven dialogue with human feedback" |
| }, |
| "2306.07932": { |
| "arxivId": "2306.07932", |
| "title": "Human-in-the-loop through chain-of-thought" |
| }, |
| "1901.05415": { |
| "arxivId": "1901.05415", |
| "title": "Learning from dialogue after deployment: Feed yourself, chatbot!" |
| }, |
| "1711.04090": { |
| "arxivId": "1711.04090", |
| "title": "MojiTalk: Generating emotional responses at scale" |
| }, |
| "1908.07687": { |
| "arxivId": "1908.07687", |
| "title": "MoEL: Mixture of empathetic listeners" |
| }, |
| "2010.01454": { |
| "arxivId": "2010.01454", |
| "title": "MIME: Mimicking emotions for empathetic response generation" |
| }, |
| "2109.05739": { |
| "arxivId": "2109.05739", |
| "title": "CEM: Commonsense-aware empathetic response generation" |
| }, |
| "1902.00506": { |
| "arxivId": "1902.00506", |
| "title": "The hanabi challenge: A new frontier for ai research" |
| }, |
| "1906.06725": { |
| "arxivId": "1906.06725", |
| "title": "Persuasion for good: Towards a personalized persuasive dialogue system for social good" |
| }, |
| "2206.14576": { |
| "arxivId": "2206.14576", |
| "title": "Using cognitive psychology to understand GPT-3" |
| }, |
| "2207.07051": { |
| "arxivId": "2207.07051", |
| "title": "Language models show human-like content effects on reasoning" |
| }, |
| "2303.11436": { |
| "arxivId": "2303.11436", |
| "title": "Mind meets machine: Unravelling GPT-4's cognitive psychology" |
| }, |
| "2303.13988": { |
| "arxivId": "2303.13988", |
| "title": "Machine psychology: Investigating emergent capabilities and behavior in large language models using psychological methods" |
| }, |
| "2307.09042": { |
| "arxivId": "2307.09042", |
| "title": "Emotional intelligence of large language models" |
| }, |
| "2212.10276": { |
| "arxivId": "2212.10276", |
| "title": "Identifying and manipulating the personality traits of language models" |
| }, |
| "2307.16180": { |
| "arxivId": "2307.16180", |
| "title": "Do LLMs possess a personality? Making the MBTI test an amazing evaluation for large language models" |
| }, |
| "2307.00184": { |
| "arxivId": "2307.00184", |
| "title": "Personality traits in large language models" |
| }, |
| "1806.11532": { |
| "arxivId": "1806.11532", |
| "title": "TextWorld: A learning environment for text-based games" |
| }, |
| "1903.03094": { |
| "arxivId": "1903.03094", |
| "title": "Learning to speak and act in a fantasy text adventure game" |
| }, |
| "1909.05398": { |
| "arxivId": "1909.05398", |
| "title": "Interactive fiction games: A colossal adventure" |
| }, |
| "2308.01404": { |
| "arxivId": "2308.01404", |
| "title": "Hoodwinked: Deception and cooperation in a text-based game for language models" |
| }, |
| "2309.01918": { |
| "arxivId": "2309.01918", |
| "title": "RoboAgent: Generalization and efficiency in robot manipulation via semantic augmentations and action chunking" |
| }, |
| "2110.05352": { |
| "arxivId": "2110.05352", |
| "title": "All one needs to know about metaverse: A complete survey on technological singularity, virtual ecosystem, and research agenda" |
| }, |
| "2112.00861": { |
| "arxivId": "2112.00861", |
| "title": "A general language assistant as a laboratory for alignment" |
| }, |
| "2206.07550": { |
| "arxivId": "2206.07550", |
| "title": "MPI: evaluating and inducing personality in pre-trained language models" |
| }, |
| "2302.02083": { |
| "arxivId": "2302.02083", |
| "title": "Theory of mind may have spontaneously emerged in large language models" |
| }, |
| "2306.06548": { |
| "arxivId": "2306.06548", |
| "title": "Inductive reasoning in humans and large language models" |
| }, |
| "2306.07622": { |
| "arxivId": "2306.07622", |
| "title": "Human-like intuitive behavior and reasoning biases emerged in language models - and disappeared in GPT-4" |
| }, |
| "2204.12000": { |
| "arxivId": "2204.12000", |
| "title": "AI personification: Estimating the personality of language models" |
| }, |
| "1801.07243": { |
| "arxivId": "1801.07243", |
| "title": "Personalizing dialogue agents: I have a dog, do you have pets too?" |
| }, |
| "2302.10646": { |
| "arxivId": "2302.10646", |
| "title": "Playing the werewolf game with artificial intelligence for language understanding" |
| }, |
| "2112.04359": { |
| "arxivId": "2112.04359", |
| "title": "Ethical and social risks of harm from language models" |
| }, |
| "2304.05335": { |
| "arxivId": "2304.05335", |
| "title": "Toxicity in ChatGPT: Analyzing persona-assigned language models" |
| }, |
| "2102.04130": { |
| "arxivId": "2102.04130", |
| "title": "Bias out-of-the-box: An empirical analysis of intersectional occupational biases in popular generative language models" |
| }, |
| "2004.09456": { |
| "arxivId": "2004.09456", |
| "title": "StereoSet: Measuring stereotypical bias in pretrained language models" |
| }, |
| "2211.08411": { |
| "arxivId": "2211.08411", |
| "title": "Large language models struggle to learn long-tail knowledge" |
| }, |
| "2304.03738": { |
| "arxivId": "2304.03738", |
| "title": "Should ChatGPT be biased? Challenges and risks of bias in large language models" |
| }, |
| "2309.03876": { |
| "arxivId": "2309.03876", |
| "title": "OpinionGPT: Modelling explicit biases in instruction-tuned LLMs" |
| }, |
| "2305.14930": { |
| "arxivId": "2305.14930", |
| "title": "In-context impersonation reveals large language models' strengths and biases" |
| }, |
| "2304.00416": { |
| "arxivId": "2304.00416", |
| "title": "Towards healthy AI: large language models need therapists too" |
| }, |
| "2106.13219": { |
| "arxivId": "2106.13219", |
| "title": "Towards understanding and mitigating social biases in language models" |
| }, |
| "1711.09050": { |
| "arxivId": "1711.09050", |
| "title": "Ethical challenges in data-driven dialogue systems" |
| }, |
| "2202.05520": { |
| "arxivId": "2202.05520", |
| "title": "What does it mean for a language model to preserve privacy?" |
| }, |
| "2210.13382": { |
| "arxivId": "2210.13382", |
| "title": "Emergent world representations: Exploring a sequence model trained on a synthetic task" |
| }, |
| "2212.08073": { |
| "arxivId": "2212.08073", |
| "title": "Constitutional AI: harmlessness from AI feedback" |
| }, |
| "2204.05862": { |
| "arxivId": "2204.05862", |
| "title": "Training a helpful and harmless assistant with reinforcement learning from human feedback" |
| }, |
| "2305.14938": { |
| "arxivId": "2305.14938", |
| "title": "Do LLMs understand social knowledge? Evaluating the sociability of large language models with SOCKET benchmark" |
| }, |
| "2112.05843": { |
| "arxivId": "2112.05843", |
| "title": "Am I me or you? State-of-the-art dialogue models cannot maintain an identity" |
| }, |
| "2209.07858": { |
| "arxivId": "2209.07858", |
| "title": "Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned" |
| }, |
| "2207.05221": { |
| "arxivId": "2207.05221", |
| "title": "Language models (mostly) know what they know" |
| }, |
| "1801.10112": { |
| "arxivId": "1801.10112", |
| "title": "Riemannian walk for incremental learning: Understanding forgetting and intransigence" |
| }, |
| "1312.6199": { |
| "arxivId": "1312.6199", |
| "title": "Intriguing properties of neural networks" |
| }, |
| "1412.6572": { |
| "arxivId": "1412.6572", |
| "title": "Explaining and harnessing adversarial examples" |
| }, |
| "1706.06083": { |
| "arxivId": "1706.06083", |
| "title": "Towards deep learning models resistant to adversarial attacks" |
| }, |
| "2112.00639": { |
| "arxivId": "2112.00639", |
| "title": "A systematic review of robustness in deep learning for computer vision: Mind the gap?" |
| }, |
| "1903.12261": { |
| "arxivId": "1903.12261", |
| "title": "Benchmarking neural network robustness to common corruptions and perturbations" |
| }, |
| "2112.08313": { |
| "arxivId": "2112.08313", |
| "title": "Measure and improve robustness in NLP models: A survey" |
| }, |
| "1812.05271": { |
| "arxivId": "1812.05271", |
| "title": "TextBugger: Generating adversarial text against real-world applications" |
| }, |
| "1909.11764": { |
| "arxivId": "1909.11764", |
| "title": "FreeLB: Enhanced adversarial training for natural language understanding" |
| }, |
| "1703.02702": { |
| "arxivId": "1703.02702", |
| "title": "Robust adversarial reinforcement learning" |
| }, |
| "2204.12581": { |
| "arxivId": "2204.12581", |
| "title": "RAMBO-RL: robust adversarial model-based offline reinforcement learning" |
| }, |
| "2208.05129": { |
| "arxivId": "2208.05129", |
| "title": "Robust reinforcement learning using offline data" |
| }, |
| "1907.11932": { |
| "arxivId": "1907.11932", |
| "title": "Is BERT really robust? A strong baseline for natural language attack on text classification and entailment" |
| }, |
| "2306.04528": { |
| "arxivId": "2306.04528", |
| "title": "PromptBench: Towards evaluating the robustness of large language models on adversarial prompts" |
| }, |
| "2303.00293": { |
| "arxivId": "2303.00293", |
| "title": "How robust is GPT-3.5 to predecessors? A comprehensive study on language understanding tasks" |
| }, |
| "1708.06733": { |
| "arxivId": "1708.06733", |
| "title": "BadNets: Identifying vulnerabilities in the machine learning model supply chain" |
| }, |
| "2006.01043": { |
| "arxivId": "2006.01043", |
| "title": "BadNL: Backdoor attacks against NLP models with semantic-preserving improvements" |
| }, |
| "2109.10855": { |
| "arxivId": "2109.10855", |
| "title": "BFClass: A backdoor-free text classification framework" |
| }, |
| "2209.01882": { |
| "arxivId": "2209.01882", |
| "title": "PromptAttack: Prompt-based attack for language models via gradient search" |
| }, |
| "2211.09527": { |
| "arxivId": "2211.09527", |
| "title": "Ignore previous prompt: Attack techniques for language models" |
| }, |
| "2201.10474": { |
| "arxivId": "2201.10474", |
| "title": "Whose language counts as high quality? measuring language ideologies in text data selection" |
| }, |
| "2306.05499": { |
| "arxivId": "2306.05499", |
| "title": "Prompt injection attack against LLM-integrated applications" |
| }, |
| "1801.01944": { |
| "arxivId": "1801.01944", |
| "title": "Audio adversarial examples: Targeted attacks on speech-to-text" |
| }, |
| "2012.15699": { |
| "arxivId": "2012.15699", |
| "title": "Better robustness by more coverage: Adversarial and mixup data augmentation for robust finetuning" |
| }, |
| "1805.12152": { |
| "arxivId": "1805.12152", |
| "title": "Robustness may be at odds with accuracy" |
| }, |
| "1901.08573": { |
| "arxivId": "1901.08573", |
| "title": "Theoretically principled trade-off between robustness and accuracy" |
| }, |
| "2009.05835": { |
| "arxivId": "2009.05835", |
| "title": "How much can we really trust you? towards simple, interpretable trust quantification metrics for deep neural networks" |
| }, |
| "2305.11391": { |
| "arxivId": "2305.11391", |
| "title": "A survey of safety and trustworthiness of large language models through the lens of verification and validation" |
| }, |
| "2211.00151": { |
| "arxivId": "2211.00151", |
| "title": "A close look into the calibration of pre-trained language models" |
| }, |
| "2006.03955": { |
| "arxivId": "2006.03955", |
| "title": "Detecting emergent intersectional biases: Contextualized word embeddings contain a distribution of human-like biases" |
| }, |
| "1607.06520": { |
| "arxivId": "1607.06520", |
| "title": "Man is to computer programmer as woman is to homemaker? debiasing word embeddings" |
| }, |
| "1608.07187": { |
| "arxivId": "1608.07187", |
| "title": "Semantics derived automatically from language corpora contain human-like biases" |
| }, |
| "2305.15852": { |
| "arxivId": "2305.15852", |
| "title": "Self-contradictory hallucinations of large language models: Evaluation, detection and mitigation" |
| }, |
| "2005.00661": { |
| "arxivId": "2005.00661", |
| "title": "On faithfulness and factuality in abstractive summarization" |
| }, |
| "2307.03987": { |
| "arxivId": "2307.03987", |
| "title": "A stitch in time saves nine: Detecting and mitigating hallucinations of LLMs by validating low-confidence generation" |
| }, |
| "1802.07228": { |
| "arxivId": "1802.07228", |
| "title": "The malicious use of artificial intelligence: Forecasting, prevention, and mitigation" |
| }, |
| "2108.07258": { |
| "arxivId": "2108.07258", |
| "title": "On the opportunities and risks of foundation models" |
| }, |
| "2305.15336": { |
| "arxivId": "2305.15336", |
| "title": "From text to MITRE techniques: Exploring the malicious use of large language models for generating cyber attack payloads" |
| }, |
| "2201.03514": { |
| "arxivId": "2201.03514", |
| "title": "Black-box tuning for language-model-as-a-service" |
| }, |
| "1611.02779": { |
| "arxivId": "1611.02779", |
| "title": "RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning" |
| }, |
| "1911.12543": { |
| "arxivId": "1911.12543", |
| "title": "How Can We Know What Language Models Know?" |
| }, |
| "2004.10964": { |
| "arxivId": "2004.10964", |
| "title": "Don\u2019t Stop Pretraining: Adapt Language Models to Domains and Tasks" |
| }, |
| "1912.08226": { |
| "arxivId": "1912.08226", |
| "title": "Meshed-Memory Transformer for Image Captioning" |
| }, |
| "2305.14318": { |
| "arxivId": "2305.14318", |
| "title": "CREATOR: Tool Creation for Disentangling Abstract and Concrete Reasoning of Large Language Models" |
| }, |
| "2011.13922": { |
| "arxivId": "2011.13922", |
| "title": "VLN\u21bbBERT: A Recurrent Vision-and-Language BERT for Navigation" |
| }, |
| "2212.10560": { |
| "arxivId": "2212.10560", |
| "title": "Self-Instruct: Aligning Language Models with Self-Generated Instructions" |
| }, |
| "2303.03480": { |
| "arxivId": "2303.03480", |
| "title": "Can an Embodied Agent Find Your \u201cCat-shaped Mug\u201d? LLM-Based Zero-Shot Object Navigation" |
| }, |
| "1907.12108": { |
| "arxivId": "1907.12108", |
| "title": "CAiRE: An Empathetic Neural Chatbot." |
| }, |
| "2212.10529": { |
| "arxivId": "2212.10529", |
| "title": "Is GPT-3 a Psychopath? Evaluating Large Language Models from a Psychological Perspective" |
| }, |
| "2205.10228": { |
| "arxivId": "2205.10228", |
| "title": "You Don\u2019t Know My Favorite Color: Preventing Dialogue Representations from Revealing Speakers\u2019 Private Personas" |
| }, |
| "2210.03735": { |
| "arxivId": "2210.03735", |
| "title": "\"Help Me Help the AI\": Understanding How Explainability Can Support Human-AI Interaction" |
| }, |
| "2203.01677": { |
| "arxivId": "2203.01677", |
| "title": "Detection of Word Adversarial Examples in Text Classification: Benchmark and Baseline via Robust Density Estimation" |
| }, |
| "2011.10492": { |
| "arxivId": "2011.10492", |
| "title": "A Sweet Rabbit Hole by DARCY: Using Honeypots to Detect Universal Trigger\u2019s Adversarial Attacks" |
| }, |
| "2005.14050": { |
| "arxivId": "2005.14050", |
| "title": "Language (Technology) is Power: A Critical Survey of \u201cBias\u201d in NLP" |
| }, |
| "2103.00020": { |
| "arxivId": "2103.00020", |
| "title": "Learning Transferable Visual Models From Natural Language Supervision" |
| }, |
| "1908.02265": { |
| "arxivId": "1908.02265", |
| "title": "ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks" |
| }, |
| "2306.05685": { |
| "arxivId": "2306.05685", |
| "title": "Judging LLM-as-a-judge with MT-Bench and Chatbot Arena" |
| }, |
| "1908.08530": { |
| "arxivId": "1908.08530", |
| "title": "VL-BERT: Pre-training of Generic Visual-Linguistic Representations" |
| }, |
| "2009.01325": { |
| "arxivId": "2009.01325", |
| "title": "Learning to summarize from human feedback" |
| }, |
| "2310.03744": { |
| "arxivId": "2310.03744", |
| "title": "Improved Baselines with Visual Instruction Tuning" |
| }, |
| "1811.00937": { |
| "arxivId": "1811.00937", |
| "title": "CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge" |
| }, |
| "2003.02320": { |
| "arxivId": "2003.02320", |
| "title": "Knowledge Graphs" |
| }, |
| "1502.05698": { |
| "arxivId": "1502.05698", |
| "title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks" |
| }, |
| "2303.16634": { |
| "arxivId": "2303.16634", |
| "title": "G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment" |
| }, |
| "2308.12966": { |
| "arxivId": "2308.12966", |
| "title": "Qwen-VL: A Frontier Large Vision-Language Model with Versatile Abilities" |
| }, |
| "2403.05530": { |
| "arxivId": "2403.05530", |
| "title": "Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context" |
| }, |
| "2209.06794": { |
| "arxivId": "2209.06794", |
| "title": "PaLI: A Jointly-Scaled Multilingual Language-Image Model" |
| }, |
| "2208.10442": { |
| "arxivId": "2208.10442", |
| "title": "Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks" |
| }, |
| "1810.02338": { |
| "arxivId": "1810.02338", |
| "title": "Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding" |
| }, |
| "2209.03143": { |
| "arxivId": "2209.03143", |
| "title": "AudioLM: A Language Modeling Approach to Audio Generation" |
| }, |
| "2111.02358": { |
| "arxivId": "2111.02358", |
| "title": "VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts" |
| }, |
| "2305.01937": { |
| "arxivId": "2305.01937", |
| "title": "Can Large Language Models Be an Alternative to Human Evaluations?" |
| }, |
| "2305.17926": { |
| "arxivId": "2305.17926", |
| "title": "Large Language Models are not Fair Evaluators" |
| }, |
| "2303.04048": { |
| "arxivId": "2303.04048", |
| "title": "Is ChatGPT a Good NLG Evaluator? A Preliminary Study" |
| }, |
| "1903.04497": { |
| "arxivId": "1903.04497", |
| "title": "Searching for long-lived particles beyond the Standard Model at the Large Hadron Collider" |
| }, |
| "1809.02156": { |
| "arxivId": "1809.02156", |
| "title": "Object Hallucination in Image Captioning" |
| }, |
| "2002.05867": { |
| "arxivId": "2002.05867", |
| "title": "Transformers as Soft Reasoners over Language" |
| }, |
| "2109.01247": { |
| "arxivId": "2109.01247", |
| "title": "Do Prompt-Based Models Really Understand the Meaning of Their Prompts?" |
| }, |
| "2309.00267": { |
| "arxivId": "2309.00267", |
| "title": "RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback" |
| }, |
| "2311.03079": { |
| "arxivId": "2311.03079", |
| "title": "CogVLM: Visual Expert for Pretrained Language Models" |
| }, |
| "2308.01390": { |
| "arxivId": "2308.01390", |
| "title": "OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models" |
| }, |
| "2304.15004": { |
| "arxivId": "2304.15004", |
| "title": "Are Emergent Abilities of Large Language Models a Mirage?" |
| }, |
| "2004.07347": { |
| "arxivId": "2004.07347", |
| "title": "HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data" |
| }, |
| "2007.08124": { |
| "arxivId": "2007.08124", |
| "title": "LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning" |
| }, |
| "2212.09597": { |
| "arxivId": "2212.09597", |
| "title": "Reasoning with Language Model Prompting: A Survey" |
| }, |
| "2306.13063": { |
| "arxivId": "2306.13063", |
| "title": "Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs" |
| }, |
| "2302.04166": { |
| "arxivId": "2302.04166", |
| "title": "GPTScore: Evaluate as You Desire" |
| }, |
| "2309.14525": { |
| "arxivId": "2309.14525", |
| "title": "Aligning Large Multimodal Models with Factually Augmented RLHF" |
| }, |
| "1908.06177": { |
| "arxivId": "1908.06177", |
| "title": "CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text" |
| }, |
| "2210.02875": { |
| "arxivId": "2210.02875", |
| "title": "Binding Language Models in Symbolic Languages" |
| }, |
| "2012.11014": { |
| "arxivId": "2012.11014", |
| "title": "KRISP: Integrating Implicit and Symbolic Knowledge for Open-Domain Knowledge-Based VQA" |
| }, |
| "2309.10020": { |
| "arxivId": "2309.10020", |
| "title": "Multimodal Foundation Models: From Specialists to General-Purpose Assistants" |
| }, |
| "2305.15771": { |
| "arxivId": "2305.15771", |
| "title": "On the Planning Abilities of Large Language Models - A Critical Investigation" |
| }, |
| "2401.14159": { |
| "arxivId": "2401.14159", |
| "title": "Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks" |
| }, |
| "2112.12870": { |
| "arxivId": "2112.12870", |
| "title": "Measuring Attribution in Natural Language Generation Models" |
| }, |
| "2305.11747": { |
| "arxivId": "2305.11747", |
| "title": "HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models" |
| }, |
| "2305.12295": { |
| "arxivId": "2305.12295", |
| "title": "Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning" |
| }, |
| "2311.12320": { |
| "arxivId": "2311.12320", |
| "title": "A Survey on Multimodal Large Language Models for Autonomous Driving" |
| }, |
| "2309.03882": { |
| "arxivId": "2309.03882", |
| "title": "Large Language Models Are Not Robust Multiple Choice Selectors" |
| }, |
| "2306.05301": { |
| "arxivId": "2306.05301", |
| "title": "ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases" |
| }, |
| "2109.09784": { |
| "arxivId": "2109.09784", |
| "title": "Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive Summarization" |
| }, |
| "2304.13731": { |
| "arxivId": "2304.13731", |
| "title": "Text-to-Audio Generation using Instruction-Tuned LLM and Latent Diffusion Model" |
| }, |
| "2310.01558": { |
| "arxivId": "2310.01558", |
| "title": "Making Retrieval-Augmented Language Models Robust to Irrelevant Context" |
| }, |
| "2401.01614": { |
| "arxivId": "2401.01614", |
| "title": "GPT-4V(ision) is a Generalist Web Agent, if Grounded" |
| }, |
| "2303.16104": { |
| "arxivId": "2303.16104", |
| "title": "Hallucinations in Large Multilingual Translation Models" |
| }, |
| "2304.02554": { |
| "arxivId": "2304.02554", |
| "title": "Human-like Summarization Evaluation with ChatGPT" |
| }, |
| "2310.05694": { |
| "arxivId": "2310.05694", |
| "title": "A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics" |
| }, |
| "2306.04181": { |
| "arxivId": "2306.04181", |
| "title": "Benchmarking Foundation Models with Language-Model-as-an-Examiner" |
| }, |
| "2305.13281": { |
| "arxivId": "2305.13281", |
| "title": "LM vs LM: Detecting Factual Errors via Cross Examination" |
| }, |
| "2310.00704": { |
| "arxivId": "2310.00704", |
| "title": "UniAudio: An Audio Foundation Model Toward Universal Audio Generation" |
| }, |
| "2311.10723": { |
| "arxivId": "2311.10723", |
| "title": "Large Language Models in Finance: A Survey" |
| }, |
| "2204.10757": { |
| "arxivId": "2204.10757", |
| "title": "FaithDial: A Faithful Benchmark for Information-Seeking Dialogue" |
| }, |
| "2105.00071": { |
| "arxivId": "2105.00071", |
| "title": "Evaluating Attribution in Dialogue Systems: The BEGIN Benchmark" |
| }, |
| "2305.19187": { |
| "arxivId": "2305.19187", |
| "title": "Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models" |
| }, |
| "2311.04850": { |
| "arxivId": "2311.04850", |
| "title": "Rethinking Benchmark and Contamination for Language Models with Rephrased Samples" |
| }, |
| "2210.04714": { |
| "arxivId": "2210.04714", |
| "title": "Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysis" |
| }, |
| "2311.07574": { |
| "arxivId": "2311.07574", |
| "title": "To See is to Believe: Prompting GPT-4V for Better Visual Instruction Tuning" |
| }, |
| "2404.13076": { |
| "arxivId": "2404.13076", |
| "title": "LLM Evaluators Recognize and Favor Their Own Generations" |
| }, |
| "2303.15621": { |
| "arxivId": "2303.15621", |
| "title": "ChatGPT as a Factual Inconsistency Evaluator for Abstractive Text Summarization" |
| }, |
| "2110.08222": { |
| "arxivId": "2110.08222", |
| "title": "DialFact: A Benchmark for Fact-Checking in Dialogue" |
| }, |
| "2305.13269": { |
| "arxivId": "2305.13269", |
| "title": "Chain of Knowledge: A Framework for Grounding Large Language Models with Structured Knowledge Bases" |
| }, |
| "2304.10513": { |
| "arxivId": "2304.10513", |
| "title": "Why Does ChatGPT Fall Short in Providing Truthful Answers?" |
| }, |
| "2306.00924": { |
| "arxivId": "2306.00924", |
| "title": "Minding Language Models\u2019 (Lack of) Theory of Mind: A Plug-and-Play Multi-Character Belief Tracker" |
| }, |
| "2305.18404": { |
| "arxivId": "2305.18404", |
| "title": "Conformal Prediction with Large Language Models for Multi-Choice Question Answering" |
| }, |
| "2110.01705": { |
| "arxivId": "2110.01705", |
| "title": "Let there be a clock on the beach: Reducing Object Hallucination in Image Captioning" |
| }, |
| "2404.00971": { |
| "arxivId": "2404.00971", |
| "title": "Exploring and Evaluating Hallucinations in LLM-Powered Code Generation" |
| }, |
| "2401.10529": { |
| "arxivId": "2401.10529", |
| "title": "Mementos: A Comprehensive Benchmark for Multimodal Large Language Model Reasoning over Image Sequences" |
| }, |
| "2308.04152": { |
| "arxivId": "2308.04152", |
| "title": "Fine-tuning Multimodal LLMs to Follow Zero-shot Demonstrative Instructions" |
| }, |
| "2212.10561": { |
| "arxivId": "2212.10561", |
| "title": "Parsel\ud83e\udd86: Algorithmic Reasoning with Language Models by Composing Decompositions" |
| }, |
| "2404.13565": { |
| "arxivId": "2404.13565", |
| "title": "Exploring Diverse Methods in Visual Question Answering" |
| }, |
| "2401.03428": { |
| "arxivId": "2401.03428", |
| "title": "Exploring Large Language Model based Intelligent Agents: Definitions, Methods, and Prospects" |
| }, |
| "2402.12309": { |
| "arxivId": "2402.12309", |
| "title": "TILP: Differentiable Learning of Temporal Logical Rules on Knowledge Graphs" |
| }, |
| "2404.13501": { |
| "arxivId": "2404.13501", |
| "title": "A Survey on the Memory Mechanism of Large Language Model based Agents" |
| }, |
| "2305.04400": { |
| "arxivId": "2305.04400", |
| "title": "Do Large Language Models Show Decision Heuristics Similar to Humans? A Case Study Using GPT-3.5" |
| }, |
| "2304.14732": { |
| "arxivId": "2304.14732", |
| "title": "Search-in-the-Chain: Towards the Accurate, Credible and Traceable Content Generation for Complex Knowledge-intensive Tasks" |
| }, |
| "2405.02957": { |
| "arxivId": "2405.02957", |
| "title": "Agent Hospital: A Simulacrum of Hospital with Evolvable Medical Agents" |
| }, |
| "2311.05657": { |
| "arxivId": "2311.05657", |
| "title": "Lumos: Learning Agents with Unified Data, Modular Design, and Open-Source LLMs" |
| }, |
| "2401.07324": { |
| "arxivId": "2401.07324", |
| "title": "Small LLMs Are Weak Tool Learners: A Multi-LLM Agent" |
| }, |
| "2405.17220": { |
| "arxivId": "2405.17220", |
| "title": "RLAIF-V: Aligning MLLMs through Open-Source AI Feedback for Super GPT-4V Trustworthiness" |
| }, |
| "2401.13178": { |
| "arxivId": "2401.13178", |
| "title": "AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agents" |
| }, |
| "2301.04449": { |
| "arxivId": "2301.04449", |
| "title": "Diving Deep into Modes of Fact Hallucinations in Dialogue Systems" |
| }, |
| "2401.12794": { |
| "arxivId": "2401.12794", |
| "title": "Benchmarking LLMs via Uncertainty Quantification" |
| }, |
| "2404.01230": { |
| "arxivId": "2404.01230", |
| "title": "LLM as a Mastermind: A Survey of Strategic Reasoning with Large Language Models" |
| }, |
| "2402.01864": { |
| "arxivId": "2402.01864", |
| "title": "(A)I Am Not a Lawyer, But...: Engaging Legal Experts towards Responsible LLM Policies for Legal Advice" |
| }, |
| "2404.18532": { |
| "arxivId": "2404.18532", |
| "title": "MileBench: Benchmarking MLLMs in Long Context" |
| }, |
| "2302.03791": { |
| "arxivId": "2302.03791", |
| "title": "How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Control" |
| }, |
| "2310.13486": { |
| "arxivId": "2310.13486", |
| "title": "Mind the instructions: a holistic evaluation of consistency and interactions in prompt-based learning" |
| }, |
| "2308.08239": { |
| "arxivId": "2308.08239", |
| "title": "MemoChat: Tuning LLMs to Use Memos for Consistent Long-Range Open-Domain Conversation" |
| }, |
| "2402.10978": { |
| "arxivId": "2402.10978", |
| "title": "Language Models with Conformal Factuality Guarantees" |
| }, |
| "2405.01724": { |
| "arxivId": "2405.01724", |
| "title": "Large Language Models are Inconsistent and Biased Evaluators" |
| }, |
| "2401.05268": { |
| "arxivId": "2401.05268", |
| "title": "AutoAct: Automatic Agent Learning from Scratch for QA via Self-Planning" |
| }, |
| "2402.03578": { |
| "arxivId": "2402.03578", |
| "title": "LLM Multi-Agent Systems: Challenges and Open Problems" |
| }, |
| "2403.00811": { |
| "arxivId": "2403.00811", |
| "title": "Cognitive Bias in Decision-Making with LLMs" |
| }, |
| "2402.11443": { |
| "arxivId": "2402.11443", |
| "title": "Benchmark Self-Evolving: A Multi-Agent Framework for Dynamic LLM Evaluation" |
| }, |
| "2402.11291": { |
| "arxivId": "2402.11291", |
| "title": "Puzzle Solving using Reasoning of Large Language Models: A Survey" |
| }, |
| "2405.20974": { |
| "arxivId": "2405.20974", |
| "title": "SaySelf: Teaching LLMs to Express Confidence with Self-Reflective Rationales" |
| }, |
| "2311.15548": { |
| "arxivId": "2311.15548", |
| "title": "Deficiency of Large Language Models in Finance: An Empirical Examination of Hallucination" |
| }, |
| "2310.16035": { |
| "arxivId": "2310.16035", |
| "title": "What's Left? Concept Grounding with Logic-Enhanced Foundation Models" |
| }, |
| "2407.00128": { |
| "arxivId": "2407.00128", |
| "title": "When Search Engine Services meet Large Language Models: Visions and Challenges" |
| }, |
| "2403.03031": { |
| "arxivId": "2403.03031", |
| "title": "Learning to Use Tools via Cooperative and Interactive Agents" |
| }, |
| "2401.09334": { |
| "arxivId": "2401.09334", |
| "title": "Large Language Models Are Neurosymbolic Reasoners" |
| }, |
| "2205.01068": { |
| "arxivId": "2205.01068", |
| "title": "OPT: Open Pre-trained Transformer Language Models" |
| }, |
| "1606.06565": { |
| "arxivId": "1606.06565", |
| "title": "Concrete Problems in AI Safety" |
| }, |
| "2201.08239": { |
| "arxivId": "2201.08239", |
| "title": "LaMDA: Language Models for Dialog Applications" |
| }, |
| "1602.03506": { |
| "arxivId": "1602.03506", |
| "title": "Research Priorities for Robust and Beneficial Artificial Intelligence" |
| }, |
| "2307.10169": { |
| "arxivId": "2307.10169", |
| "title": "Challenges and Applications of Large Language Models" |
| }, |
| "2304.11082": { |
| "arxivId": "2304.11082", |
| "title": "Fundamental Limitations of Alignment in Large Language Models" |
| }, |
| "1709.02788": { |
| "arxivId": "1709.02788", |
| "title": "IFTTT vs. Zapier: A Comparative Study of Trigger-Action Programming Frameworks" |
| }, |
| "2310.03659": { |
| "arxivId": "2310.03659", |
| "title": "Balancing Autonomy and Alignment: A Multi-Dimensional Taxonomy for Autonomous LLM-powered Multi-Agent Architectures" |
| } |
| } |