File size: 2,939 Bytes
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"name": "DynaMath",
"release_date": "2024-05-01",
"subsets": {
"main": {
"language": [
"en"
],
"modalities": [
"single_image_start"
],
"task_type": "short_answer_qa",
"score_pipeline": [
"rule-match",
"llm-match"
],
"score_protocol": {
"reference": "VLMEvalKit@vlmeval/dataset/dynamath.py:58-109 — rule parse of the answer first (JSON 'short answer' / direct parse_answer), auxiliary LLM extraction only on parse failure, then rule compare: float abs(diff)<=0.001, MC letter equality, text containment. Official DynaMath@evaluation/gpt4o/gpt_4o_json_eval.py:98-123 uses the same JSON 'short answer' parse + abs(diff)<=0.001 rule compare. [Re-verified in clone 2026-07-07: DynaMath_auxeval at dynamath.py:58-110 — abs(diff)<=0.001 at :92.]",
"note": "Official grading is rule-based over a JSON-formatted answer forced by their inference prompt; the mm-eval prompt (lmms-eval style question+Options trailer) does not force JSON, so LLM extraction fallback (VLMEvalKit protocol) is required. Official float tolerance is ABSOLUTE 0.001 (score_params.numeric_abs_tol), not relative. Official headline metrics are average-case AND worst-case accuracy over 10 generated variants per seed question; the mm-eval copy ships 501 rows (seed variant only), so worst-case accuracy is not reproducible."
},
"prompt_template": "<image>{{ question }}{% if options %}\nOptions:\n{% for k, v in options.items() %}{{ k }}. {{ v }}{% if not loop.last %}\n{% endif %}{% endfor %}{% endif %}\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": {
"subject": {
"from": "subject"
},
"level": {
"from": "level"
},
"answer_type": {
"from": "answer_type"
}
},
"source": {
"format": "json",
"url": {
"test": "https://github.com/DynaMath/DynaMath/tree/main/dataset"
}
}
},
"prompt_template_source": {
"origin": "official",
"reference": "https://github.com/EvolvingLMMs-Lab/lmms-eval/blob/main/lmms_eval/tasks/dynamath/utils.py (dynamath_doc_to_text — image+question+optional Options:/A./B. trailer)",
"notes": "Tier 4: lmms-eval DynaMath canonical evaluation prompt."
},
"score_params": {
"numeric_abs_tol": 0.001
}
}
}
} |