| { | |
| "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 | |
| } | |
| } | |
| } | |
| } |