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"name": "We-Math",
"release_date": "2024-07-01",
"subsets": {
"main": {
"language": [
"en"
],
"modalities": [
"single_image_start"
],
"task_type": "multiple_choice_qa",
"score_pipeline": [
"exact-match",
"rule-match"
],
"score_protocol": {
"reference": "official We-Math/We-Math@README + evaluation/four_dimensional_metrics.py & evaluation/accuracy.py — 'we use string matching to directly extract answers, which eliminates the high cost of using additional models for further answer extraction' (rule-based option-letter extraction, exact compare). [Quote re-verified verbatim in the official README 2026-07-07; rule pipeline re-verified vendored at VLMEvalKit@vlmeval/dataset/utils/wemath.py:28-41 (split on 'Answer', strip, first char in A-H, compare).]",
"note": "Official headline is the four-dimensional metric set (IK/IG/CM/RM) plus step-wise accuracy, computed by grouping one-step sub-problems with their multi-step composite problem (problem_id/step_key, vendored in VLMEvalKit vlmeval/dataset/utils/wemath.py:16-238); a per-sample accuracy scorer will not reproduce those group scores (grouping keys ARE shipped in extra). VLMEvalKit grades WeMath via mcq_vanilla_eval (image_mcq.py WeMath.evaluate -> utils/multiple_choice.py mcq_vanilla_eval, rule can_infer + GPT extraction fallback), i.e. llm_extract — disagrees with the official rule-only protocol; official preferred."
},
"prompt_template": "<image>{{ question }}\nOptions:\n{% for k, v in options.items() %}{{ k }}. {{ v }}{% if not loop.last %}\n{% endif %}{% endfor %}\nPlease select the correct answer from the options above. \n",
"mapping_from_source": {
"media": {
"from": "image",
"type": "list",
"min_items": 1,
"max_items": 1
},
"id": {
"from": "id"
},
"question": {
"from": "question"
},
"answer": {
"from": "answer",
"optional": true
},
"options": {
"from": "options",
"optional": true,
"note": "list source values are normalized to {A,B,...} dict"
},
"extra": {
"knowledge_concept": {
"from": "knowledge_concept"
},
"problem_id": {
"from": "problem_id"
},
"step_key": {
"from": "step_key"
}
},
"source": {
"format": "huggingface",
"url": {
"testmini": "https://huggingface.co/datasets/We-Math/We-Math"
}
}
},
"prompt_template_source": {
"origin": "official",
"reference": "https://github.com/open-compass/VLMEvalKit/blob/main/vlmeval/dataset/image_mcq.py (ImageMCQDataset.build_prompt — canonical MCQ trailer with options bullets)",
"notes": "Tier 3: VLMEvalKit ImageMCQDataset.build_prompt; We-Math is evaluated via VLMEvalKit's standard MCQ flow."
}
}
}
} |