{
 "name": "MathVerse",
 "release_date": "2024-03-22",
 "subsets": {
  "testmini": {
   "language": [
    "en"
   ],
   "modalities": [
    "single_image_start"
   ],
   "task_type": "multiple_choice_qa",
   "score_type": "rule_llm_judge",
   "score_protocol": {
    "reference": "VLMEvalKit@vlmeval/dataset/utils/mathverse.py:123-167 — post_check_score exact-match prefetch, else GPT answer extraction (MathVerse_auxeval_extract) then GPT binary 0/1 correctness judgement (MathVerse_auxeval_score); same two-stage extract+score design as official ZrrSkywalker/MathVerse evaluation (extract_answer.py + score_answer.py). lmms-eval@lmms_eval/tasks/mathverse/mathverse_evals.py:92-110 judges every sample binary via LLM (quick_match optional exact match). [Re-verified in clone 2026-07-07: post_check_score at mathverse.py:126-133, auxeval_extract/score at 136-170; lmms score_answer at mathverse_evals.py:99-110.]",
    "note": "Official protocol judges with an LLM after LLM extraction (binary 0/1); VLMEvalKit pre-resolves exact string matches by rule, which this classification adopts. mm-eval publishes the answer-only protocol (query_wo prompts, 'please directly answer'), not the official CoT eval (query_cot + multi-step CoT judging) — scores are comparable to VLMEvalKit MathVerse_MINI, not to the paper's CoT-E numbers. The HF repo also carries a testmini_text_only config (788 rows, no image) that metadata.json does not describe as a subset."
   },
   "prompt_template": "<image>{{ question }}",
   "prompt_template_source": {
    "origin": "source_column",
    "reference": "https://huggingface.co/datasets/AI4Math/MathVerse (query_wo column = pre-rendered eval prompt)",
    "notes": "Tier 2: MathVerse provides query_wo column which is the pre-rendered single-answer evaluation prompt produced by the official eval pipeline; we use it verbatim and prepend <image>."
   },
   "mapping_from_source": {
    "media": {
     "from": "image",
     "type": "list",
     "min_items": 1,
     "max_items": 1
    },
    "id": {
     "from": "sample_index"
    },
    "question": {
     "from": "query_wo"
    },
    "answer": {
     "from": "answer",
     "optional": true
    },
    "extra": {
     "question_type": {
      "from": "question_type"
     },
     "problem_version": {
      "from": "problem_version"
     },
     "problem_index": {
      "from": "problem_index"
     },
     "source": {
      "from": "source",
      "note": "v2 message pass-through moved to the flat top level (format v3 re-emission)"
     },
     "subfield": {
      "from": "subfield",
      "note": "v2 message pass-through moved to the flat top level (format v3 re-emission)"
     },
     "subject": {
      "from": "subject",
      "note": "v2 message pass-through moved to the flat top level (format v3 re-emission)"
     }
    },
    "source": {
     "format": "huggingface",
     "url": {
      "testmini": "https://huggingface.co/datasets/AI4Math/MathVerse"
     }
    }
   }
  },
  "testmini_text_only": {
   "language": [
    "en"
   ],
   "modalities": [
    "text"
   ],
   "task_type": "multiple_choice_qa",
   "score_type": "rule_llm_judge",
   "score_protocol": {
    "reference": "VLMEvalKit@vlmeval/dataset/utils/mathverse.py:123-167 — post_check_score exact-match prefetch, else GPT answer extraction (MathVerse_auxeval_extract) then GPT binary 0/1 correctness judgement (MathVerse_auxeval_score); same two-stage extract+score design as official ZrrSkywalker/MathVerse evaluation (extract_answer.py + score_answer.py). lmms-eval@lmms_eval/tasks/mathverse/mathverse_evals.py:92-110 judges every sample binary via LLM (quick_match optional exact match). [Re-verified in clone 2026-07-07: post_check_score at mathverse.py:126-133, auxeval_extract/score at 136-170; lmms score_answer at mathverse_evals.py:99-110.]",
    "note": "Same protocol as the testmini subset (answer-only query_wo prompts, two-stage LLM extract+score). This subset IS the repo's testmini_text_only config (788 rows, text-dominant problems without the diagram image), previously published as data but undescribed in metadata.json; declared as a subset at the format-v3 re-emission."
   },
   "prompt_template": "{{ question }}",
   "prompt_template_source": {
    "origin": "source_column",
    "reference": "https://huggingface.co/datasets/AI4Math/MathVerse (query_wo column = pre-rendered eval prompt)",
    "notes": "Tier 2: MathVerse provides query_wo column which is the pre-rendered single-answer evaluation prompt produced by the official eval pipeline; we use it verbatim. Text-only variant rows carry no media, so no <image> placeholder is prepended."
   },
   "mapping_from_source": {
    "media": {
     "from": "image",
     "type": "list",
     "min_items": 0,
     "max_items": 0
    },
    "id": {
     "from": "sample_index"
    },
    "question": {
     "from": "query_wo"
    },
    "answer": {
     "from": "answer",
     "optional": true
    },
    "extra": {
     "question_type": {
      "from": "question_type"
     },
     "problem_version": {
      "from": "problem_version"
     },
     "problem_index": {
      "from": "problem_index"
     },
     "source": {
      "from": "source",
      "note": "v2 message pass-through moved to the flat top level (format v3 re-emission)"
     },
     "subfield": {
      "from": "subfield",
      "note": "v2 message pass-through moved to the flat top level (format v3 re-emission)"
     },
     "subject": {
      "from": "subject",
      "note": "v2 message pass-through moved to the flat top level (format v3 re-emission)"
     }
    },
    "source": {
     "format": "huggingface",
     "url": {
      "testmini_text_only": "https://huggingface.co/datasets/AI4Math/MathVerse"
     }
    }
   }
  }
 }
}