Add manifest (metadata.json)
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metadata.json
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}
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{
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"name": "EMMA",
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"release_date": "2026-07-07",
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"subsets": {
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"main": {
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"language": [
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"en"
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],
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"modalities": [
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"multi_image_interleave",
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"text"
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],
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"task_type": "multiple_choice_qa",
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"score_type": "rule_llm_judge",
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"score_protocol": {
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"reference": "official EMMA-Bench/EMMA@evaluation/evaluate.py — rule-based fast_extract_answer (letter/number/boxed/'answer is' patterns) + is_equal compare by default; optional --gpt_eval mode where GPT-4o judges and 'correct' in the response decides the score; evaluation/calculate_acc.py aggregates. lmms-eval@lmms_eval/tasks/emma/utils.py:159-200 (LLM judge with fast_extract_answer+is_equal fallback, emma_all.yaml use_lmms_judge: True).",
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"note": "Framework implementations lean judge-heavier than the official default: lmms-eval judges every sample (use_lmms_judge), VLMEvalKit EMMADataset inherits ImageShortQADataset's all-LLM binary judge (image_shortqa.py:15-48). Classified rule_llm_judge: deterministic extraction resolves direct letter/short answers (the published prompt requests direct answers), LLM resolves the remainder — consistent with official fast-eval + LLMs-eval pairing. EMMA is mixed MCQ/open (options optional); task_type multiple_choice as majority format (2002/2788 rows are Multiple Choice)."
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},
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"prompt_template": "{% if context %}{{ context }}\n{% endif %}{{ question }}{% if options %}\n\n{% for k, v in options.items() %}({{ k }}) {{ v }}\n{% endfor %}\nAnswer with the option's letter from the given choices directly.{% else %}\n\nAnswer the question using a single word or phrase.{% endif %}",
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"prompt_template_source": {
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"origin": "official",
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"reference": "https://huggingface.co/datasets/luckychao/EMMA; context+question+options structure per official EMMA-Bench/EMMA@data_utils.py L25-52 (build_query: '{context}\\n{question}\\n{options}\\n...') and lmms-eval@lmms_eval/tasks/emma/utils.py L27-31",
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"notes": "Tier 1: EMMA uses MMMU-style <image_N> markers in question/options; MCQ letter-answer convention. Re-conversion 2026-07-07: restored the options block data (options were dropped in the previous upload) and prepended '{% if context %}{{ context }}\\n{% endif %}' because the official build_query renders context before the question (57 Physics rows carry a non-empty context block of physical constants that the previous upload dropped). The direct-answer trailers are kept from the previously published template; the official EMMA eval prompt additionally requests \\boxed{} + CoT (configs/gpt.yaml) — intentional deviation preserved from the published copy."
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},
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"mapping_from_source": {
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"source": {
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"format": "json",
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"url": {
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"test": "https://huggingface.co/datasets/luckychao/EMMA"
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}
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},
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"id": {
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"from": "id",
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"note": "source column 'pid' (e.g. Math_1, chem_1, coding_1, phy_1), copied verbatim during pre-download."
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},
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"question": {
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"from": "question",
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"note": "<image_N> refs normalized to the canonical <image> placeholder; referenced images materialized into media in textual order."
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},
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"options": {
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"from": "options",
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"optional": true,
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"note": "list source values are normalized to {A,B,...} dict. 301 rows reference images inside option strings via <image_N>; those refs are normalized to <image> and the referenced images are appended to media in option order (after question refs), so placeholder count == media count. 4 rows repeat a ref (chem_115, chem_139, Math_105, Math_544) — the repeated image is duplicated in media, whereas the official pipeline deduplicates and sends it once."
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},
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"answer": {
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"from": "answer",
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"optional": true
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},
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"media": {
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"from": "media",
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"type": "list",
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"min_items": 0,
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"max_items": 5,
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"note": "materialized during pre-download from source columns image_1..image_5, one entry per <image_N> occurrence across context/question/options in template render order. chem_146 references no images anywhere (its 2 source images are unreferenced) and is a text-only row with media=[], mirroring the official pipeline which sends no image for it."
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},
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"extra": {
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"context": {
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"from": "context"
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},
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"type": {
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"from": "type"
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},
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"subject": {
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"from": "subject"
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},
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"task": {
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"from": "task"
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},
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"category": {
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"from": "category"
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},
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"source_dataset": {
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"from": "source_dataset"
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},
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"solution": {
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"from": "solution"
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}
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}
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}
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}
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}
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}
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