| { | |
| "name": "AI2D", | |
| "release_date": "2016-08-26", | |
| "subsets": { | |
| "main": { | |
| "language": [ | |
| "en" | |
| ], | |
| "modalities": [ | |
| "single_image_start" | |
| ], | |
| "task_type": "multiple_choice_qa", | |
| "score_pipeline": [ | |
| "rule-match", | |
| "llm-match" | |
| ], | |
| "score_protocol": { | |
| "reference": "vlmevalkit@vlmeval/dataset/image_mcq.py:249-324 (ImageMCQDataset.evaluate_heuristic) -> vlmevalkit@vlmeval/dataset/utils/multiple_choice.py:475-508 mcq_vanilla_eval: can_infer rule prefetch, then GPT extractor (extract_answer_from_item @vlmeval/dataset/utils/multiple_choice.py:359-407) maps verbose output to a letter, rule-compare vs GT; AI2D_TEST registered @vlmeval/dataset/image_mcq.py:100. Cross-check: lmms-eval@lmms_eval/tasks/ai2d/ai2d.yaml:41-55 (MultiChoiceRegexFilter + exact_match, pure rule)", | |
| "note": "mm-eval prompt follows the lmms-eval trailer; lmms-eval grades AI2D with deterministic regex extraction only, VLMEvalKit adds the GPT extraction fallback. Both are plain per-sample accuracy — no aggregation caveat." | |
| }, | |
| "prompt_template": "<image>{{ question }}\n{% for k, v in options.items() %}{{ k }}. {{ v }}\n{% endfor %}Answer with the option's letter from the given choices directly.", | |
| "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" | |
| }, | |
| "source": { | |
| "format": "huggingface", | |
| "url": { | |
| "test": "https://huggingface.co/datasets/lmms-lab/ai2d" | |
| } | |
| } | |
| }, | |
| "prompt_template_source": { | |
| "origin": "official", | |
| "reference": "https://github.com/EvolvingLMMs-Lab/lmms-eval/blob/main/lmms_eval/tasks/ai2d/utils.py (ai2d_doc_to_text — 'A./B./...' options + 'Answer with the option's letter from the given choices directly.' canonical trailer)", | |
| "notes": "Tier 4: lmms-eval AI2D canonical evaluation prompt." | |
| } | |
| } | |
| } | |
| } |