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"prompt_template_source": {
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"origin": "official",
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"reference": "https://github.com/naver-ai/tablevqabench/blob/master/src/example_prompts.py (FINTABNETQA_PROMPT L33-L46)",
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"notes": "Tier 1: the authors' official per-split few-shot vision prompt, deployed verbatim (jinja-ified {question} -> {{ question }}; <image> placeholder prepended). Restructured from one 'main' subset into one subset per official split on 2026-07-07 so each split renders its official prompt."
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}
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}
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}
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{
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"name": "TableVQA-Bench",
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"release_date": "2024-04-01",
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"subsets": {
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"vwtq": {
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"language": [
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"en"
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],
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"modalities": [
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"single_image_start"
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],
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"task_type": "short_answer_qa",
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"score_type": "rule",
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"score_protocol": {
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"reference": "Official naver-ai/tablevqabench src/evaluate_tablevqa_dataset.py:17-33 (per-split dispatch) — vwtq/vwtq_syn use WikiTableQuestions denotation matching (to_value_list/check_denotation with number/date normalization); vtabfact scores lowercase 'true'/'false' containment in the prediction (evaluate_tabfact, lines 45-64); fintabnetqa uses normalized exact match with $/unit-word stripping ('relieved accuracy'). Ported in VLMEvalKit@vlmeval/dataset/image_vqa.py:2366-2396 + vlmeval/dataset/utils/tablevqabench.py:54-158. Headline = mean accuracy per split.",
|
| 16 |
+
"note": "Fully deterministic (rule) but with per-split normalization the generic exact/template chain will NOT reproduce: WTQ denotation normalization and fintabnet unit stripping. vtabfact gt is stored as 'true'/'false' (mapped from source gt '1'/'0'), matching what the official evaluator expects from a generative model: evaluate_tabfact (src/evaluate_tablevqa_dataset.py:56-64) lowercases the prediction and scores \"'true' in pred and gt==1\" / \"'false' in pred and gt==0\" as correct, and the official VTABFACT_PROMPT (src/example_prompts.py:21-29) instructs 'You should only answer true or false.'. Official also uses split-specific prompts (VTABFACT_PROMPT etc., image_vqa.py:2398-2414) whereas the published prompt_template is the generic short-answer prompt. Official reporting is per-split accuracy averaged over 4 splits; published copy stores splits as HF splits under one subset."
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},
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"prompt_template": "<image>You are asked to answer questions asked on an image.\nYou should answer the question with a single word.\nExample: \nQuestion: what was the only year mr. wu competed in the olympic games?\nAnswer: 2004\nQuestion: which township in pope county, arkansas has the least amount of water area?\nAnswer: Freeman\nIf you have multiple answers, please separate them with || marks. Example: Apple||Banana||Tomato\n\nQuestion: {{ question }}\nAnswer:",
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"mapping_from_source": {
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"media": {
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"from": "image",
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"type": "list",
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"min_items": 1,
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"max_items": 1
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},
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"id": {
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"from": "qa_id"
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},
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"question": {
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"from": "question"
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},
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"answer": {
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"from": "gt",
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"optional": true,
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"note": "vtabfact split only: source gt '1'/'0' mapped to 'true'/'false' per the official evaluator (naver-ai/tablevqabench src/evaluate_tablevqa_dataset.py:56-64: lowercase 'true'/'false' containment vs gt 1/0; src/example_prompts.py:21-29: 'You should only answer true or false.'). vwtq/vwtq_syn/fintabnetqa gts are verbatim source values."
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},
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"source": {
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"format": "huggingface",
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"url": {
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"vwtq": "https://huggingface.co/datasets/terryoo/TableVQA-Bench"
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}
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}
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},
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"prompt_template_source": {
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"origin": "official",
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"reference": "https://github.com/naver-ai/tablevqabench/blob/master/src/example_prompts.py (VWTQ_PROMPT L8-L19)",
|
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"notes": "Tier 1: the authors' official per-split few-shot vision prompt, deployed verbatim (jinja-ified {question} -> {{ question }}; <image> placeholder prepended). Restructured from one 'main' subset into one subset per official split on 2026-07-07 so each split renders its official prompt."
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+
}
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},
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"vwtq_syn": {
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"language": [
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"en"
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],
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"modalities": [
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"single_image_start"
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],
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"task_type": "short_answer_qa",
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"score_type": "rule",
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"score_protocol": {
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+
"reference": "Official naver-ai/tablevqabench src/evaluate_tablevqa_dataset.py:17-33 (per-split dispatch) — vwtq/vwtq_syn use WikiTableQuestions denotation matching (to_value_list/check_denotation with number/date normalization); vtabfact scores lowercase 'true'/'false' containment in the prediction (evaluate_tabfact, lines 45-64); fintabnetqa uses normalized exact match with $/unit-word stripping ('relieved accuracy'). Ported in VLMEvalKit@vlmeval/dataset/image_vqa.py:2366-2396 + vlmeval/dataset/utils/tablevqabench.py:54-158. Headline = mean accuracy per split.",
|
| 61 |
+
"note": "Fully deterministic (rule) but with per-split normalization the generic exact/template chain will NOT reproduce: WTQ denotation normalization and fintabnet unit stripping. vtabfact gt is stored as 'true'/'false' (mapped from source gt '1'/'0'), matching what the official evaluator expects from a generative model: evaluate_tabfact (src/evaluate_tablevqa_dataset.py:56-64) lowercases the prediction and scores \"'true' in pred and gt==1\" / \"'false' in pred and gt==0\" as correct, and the official VTABFACT_PROMPT (src/example_prompts.py:21-29) instructs 'You should only answer true or false.'. Official also uses split-specific prompts (VTABFACT_PROMPT etc., image_vqa.py:2398-2414) whereas the published prompt_template is the generic short-answer prompt. Official reporting is per-split accuracy averaged over 4 splits; published copy stores splits as HF splits under one subset."
|
| 62 |
+
},
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+
"prompt_template": "<image>You are asked to answer questions asked on an image.\nYou should answer the question with a single word.\nExample: \nQuestion: what was the only year mr. wu competed in the olympic games?\nAnswer: 2004\nQuestion: which township in pope county, arkansas has the least amount of water area?\nAnswer: Freeman\nIf you have multiple answers, please separate them with || marks. Example: Apple||Banana||Tomato\n\nQuestion: {{ question }}\nAnswer:",
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+
"mapping_from_source": {
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+
"media": {
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"from": "image",
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"type": "list",
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"min_items": 1,
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"max_items": 1
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},
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"id": {
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"from": "qa_id"
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},
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"question": {
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"from": "question"
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},
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"answer": {
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"from": "gt",
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"optional": true,
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+
"note": "vtabfact split only: source gt '1'/'0' mapped to 'true'/'false' per the official evaluator (naver-ai/tablevqabench src/evaluate_tablevqa_dataset.py:56-64: lowercase 'true'/'false' containment vs gt 1/0; src/example_prompts.py:21-29: 'You should only answer true or false.'). vwtq/vwtq_syn/fintabnetqa gts are verbatim source values."
|
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},
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+
"source": {
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"format": "huggingface",
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"url": {
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"vwtq_syn": "https://huggingface.co/datasets/terryoo/TableVQA-Bench"
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}
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+
}
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+
},
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+
"prompt_template_source": {
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"origin": "official",
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"reference": "https://github.com/naver-ai/tablevqabench/blob/master/src/example_prompts.py (VWTQ_PROMPT L8-L19 (vwtq_syn is the synthetic-style WTQ variant, same official prompt))",
|
| 92 |
+
"notes": "Tier 1: the authors' official per-split few-shot vision prompt, deployed verbatim (jinja-ified {question} -> {{ question }}; <image> placeholder prepended). Restructured from one 'main' subset into one subset per official split on 2026-07-07 so each split renders its official prompt."
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+
}
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+
},
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+
"vtabfact": {
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"language": [
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+
"en"
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],
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"modalities": [
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"single_image_start"
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+
],
|
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"task_type": "yes_no_qa",
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+
"score_type": "rule",
|
| 104 |
+
"score_protocol": {
|
| 105 |
+
"reference": "Official naver-ai/tablevqabench src/evaluate_tablevqa_dataset.py:17-33 (per-split dispatch) — vwtq/vwtq_syn use WikiTableQuestions denotation matching (to_value_list/check_denotation with number/date normalization); vtabfact scores lowercase 'true'/'false' containment in the prediction (evaluate_tabfact, lines 45-64); fintabnetqa uses normalized exact match with $/unit-word stripping ('relieved accuracy'). Ported in VLMEvalKit@vlmeval/dataset/image_vqa.py:2366-2396 + vlmeval/dataset/utils/tablevqabench.py:54-158. Headline = mean accuracy per split.",
|
| 106 |
+
"note": "Fully deterministic (rule) but with per-split normalization the generic exact/template chain will NOT reproduce: WTQ denotation normalization and fintabnet unit stripping. vtabfact gt is stored as 'true'/'false' (mapped from source gt '1'/'0'), matching what the official evaluator expects from a generative model: evaluate_tabfact (src/evaluate_tablevqa_dataset.py:56-64) lowercases the prediction and scores \"'true' in pred and gt==1\" / \"'false' in pred and gt==0\" as correct, and the official VTABFACT_PROMPT (src/example_prompts.py:21-29) instructs 'You should only answer true or false.'. Official also uses split-specific prompts (VTABFACT_PROMPT etc., image_vqa.py:2398-2414) whereas the published prompt_template is the generic short-answer prompt. Official reporting is per-split accuracy averaged over 4 splits; published copy stores splits as HF splits under one subset."
|
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+
},
|
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+
"prompt_template": "<image>You are asked to answer whether the statement is True or False based on given image\nYou should only answer true or false.\nExample: \nStatement: the milwaukee buck win 6 game in the 2010 - 11 season\nAnswer: True\nStatement: only the top team score above the average of 8.8\nAnswer: False\n\nStatement: {{ question }}\nAnswer:",
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"mapping_from_source": {
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"media": {
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"from": "image",
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"type": "list",
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"min_items": 1,
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"max_items": 1
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},
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"id": {
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"from": "qa_id"
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},
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"question": {
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"from": "question"
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},
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"answer": {
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"from": "gt",
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"optional": true,
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+
"note": "vtabfact split only: source gt '1'/'0' mapped to 'true'/'false' per the official evaluator (naver-ai/tablevqabench src/evaluate_tablevqa_dataset.py:56-64: lowercase 'true'/'false' containment vs gt 1/0; src/example_prompts.py:21-29: 'You should only answer true or false.'). vwtq/vwtq_syn/fintabnetqa gts are verbatim source values."
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},
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"source": {
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+
"format": "huggingface",
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+
"url": {
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"vtabfact": "https://huggingface.co/datasets/terryoo/TableVQA-Bench"
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}
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}
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| 133 |
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},
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"prompt_template_source": {
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"origin": "official",
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| 136 |
+
"reference": "https://github.com/naver-ai/tablevqabench/blob/master/src/example_prompts.py (VTABFACT_PROMPT L21-L31)",
|
| 137 |
+
"notes": "Tier 1: the authors' official per-split few-shot vision prompt, deployed verbatim (jinja-ified {question} -> {{ question }}; <image> placeholder prepended). Restructured from one 'main' subset into one subset per official split on 2026-07-07 so each split renders its official prompt."
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}
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},
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"fintabnetqa": {
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"language": [
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"en"
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],
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"modalities": [
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"single_image_start"
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| 146 |
+
],
|
| 147 |
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"task_type": "short_answer_qa",
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| 148 |
+
"score_type": "rule",
|
| 149 |
+
"score_protocol": {
|
| 150 |
+
"reference": "Official naver-ai/tablevqabench src/evaluate_tablevqa_dataset.py:17-33 (per-split dispatch) — vwtq/vwtq_syn use WikiTableQuestions denotation matching (to_value_list/check_denotation with number/date normalization); vtabfact scores lowercase 'true'/'false' containment in the prediction (evaluate_tabfact, lines 45-64); fintabnetqa uses normalized exact match with $/unit-word stripping ('relieved accuracy'). Ported in VLMEvalKit@vlmeval/dataset/image_vqa.py:2366-2396 + vlmeval/dataset/utils/tablevqabench.py:54-158. Headline = mean accuracy per split.",
|
| 151 |
+
"note": "Fully deterministic (rule) but with per-split normalization the generic exact/template chain will NOT reproduce: WTQ denotation normalization and fintabnet unit stripping. vtabfact gt is stored as 'true'/'false' (mapped from source gt '1'/'0'), matching what the official evaluator expects from a generative model: evaluate_tabfact (src/evaluate_tablevqa_dataset.py:56-64) lowercases the prediction and scores \"'true' in pred and gt==1\" / \"'false' in pred and gt==0\" as correct, and the official VTABFACT_PROMPT (src/example_prompts.py:21-29) instructs 'You should only answer true or false.'. Official also uses split-specific prompts (VTABFACT_PROMPT etc., image_vqa.py:2398-2414) whereas the published prompt_template is the generic short-answer prompt. Official reporting is per-split accuracy averaged over 4 splits; published copy stores splits as HF splits under one subset."
|
| 152 |
+
},
|
| 153 |
+
"prompt_template": "<image>You are asked to answer questions asked on a image.\nYou should answer the question within a single word or few words.\nIf units can be known, the answer should include units such as $, %, million and etc.\nExample: \nQuestion: What were the total financing originations for the fiscal year ended October 31, 2004?\nAnswer: $3,852 million\nQuestion: What is the time period represented in the table?\nAnswer: October 31\nQuestion: What was the percentage of net sales for selling, general and administrative expenses in 2006?\nAnswer: 34.2%\nQuestion: {{ question }}\nAnswer:",
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"mapping_from_source": {
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"media": {
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"from": "image",
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"type": "list",
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"min_items": 1,
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"max_items": 1
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},
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"id": {
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"from": "qa_id"
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},
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"question": {
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"from": "question"
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},
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"answer": {
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"from": "gt",
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"optional": true,
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| 170 |
+
"note": "vtabfact split only: source gt '1'/'0' mapped to 'true'/'false' per the official evaluator (naver-ai/tablevqabench src/evaluate_tablevqa_dataset.py:56-64: lowercase 'true'/'false' containment vs gt 1/0; src/example_prompts.py:21-29: 'You should only answer true or false.'). vwtq/vwtq_syn/fintabnetqa gts are verbatim source values."
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| 171 |
+
},
|
| 172 |
+
"source": {
|
| 173 |
+
"format": "huggingface",
|
| 174 |
+
"url": {
|
| 175 |
+
"fintabnetqa": "https://huggingface.co/datasets/terryoo/TableVQA-Bench"
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| 176 |
+
}
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|
| 177 |
}
|
| 178 |
+
},
|
| 179 |
+
"prompt_template_source": {
|
| 180 |
+
"origin": "official",
|
| 181 |
+
"reference": "https://github.com/naver-ai/tablevqabench/blob/master/src/example_prompts.py (FINTABNETQA_PROMPT L33-L46)",
|
| 182 |
+
"notes": "Tier 1: the authors' official per-split few-shot vision prompt, deployed verbatim (jinja-ified {question} -> {{ question }}; <image> placeholder prepended). Restructured from one 'main' subset into one subset per official split on 2026-07-07 so each split renders its official prompt."
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| 183 |
+
}
|
| 184 |
}
|
| 185 |
+
}
|
| 186 |
+
}
|