| --- |
| title: Optillm |
| emoji: 💬 |
| colorFrom: yellow |
| colorTo: purple |
| sdk: gradio |
| sdk_version: 4.36.1 |
| app_file: app.py |
| pinned: false |
| license: apache-2.0 |
| --- |
| |
| ## References |
|
|
| - [Fact, Fetch, and Reason: A Unified Evaluation of Retrieval-Augmented Generation](https://arxiv.org/abs/2409.12941) |
| - [Writing in the Margins: Better Inference Pattern for Long Context Retrieval](https://www.arxiv.org/abs/2408.14906) |
| - [Chain-of-Thought Reasoning Without Prompting](https://arxiv.org/abs/2402.10200) |
| - [Re-Reading Improves Reasoning in Large Language Models](https://arxiv.org/abs/2309.06275) |
| - [In-Context Principle Learning from Mistakes](https://arxiv.org/abs/2402.05403) |
| - [Planning In Natural Language Improves LLM Search For Code Generation](https://arxiv.org/abs/2409.03733) |
| - [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://arxiv.org/abs/2203.11171) |
| - [Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solvers](https://arxiv.org/abs/2408.06195) |
| - [Mixture-of-Agents Enhances Large Language Model Capabilities](https://arxiv.org/abs/2406.04692) |
| - [Prover-Verifier Games improve legibility of LLM outputs](https://arxiv.org/abs/2407.13692) |
| - [Monte Carlo Tree Search Boosts Reasoning via Iterative Preference Learning](https://arxiv.org/abs/2405.00451) |
| - [Unsupervised Evaluation of Code LLMs with Round-Trip Correctness](https://arxiv.org/abs/2402.08699) |
| - [Patched MOA: optimizing inference for diverse software development tasks](https://arxiv.org/abs/2407.18521) |
| - [Patched RTC: evaluating LLMs for diverse software development tasks](https://arxiv.org/abs/2407.16557) |
