| { |
| "name": "TableVQA-Bench", |
| "release_date": "2024-04-01", |
| "subsets": { |
| "vwtq": { |
| "language": [ |
| "en" |
| ], |
| "modalities": [ |
| "single_image_start" |
| ], |
| "task_type": "short_answer_qa", |
| "score_pipeline": [ |
| "exact-match", |
| "rule-match" |
| ], |
| "score_protocol": { |
| "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.", |
| "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." |
| }, |
| "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:", |
| "mapping_from_source": { |
| "media": { |
| "from": "image", |
| "type": "list", |
| "min_items": 1, |
| "max_items": 1 |
| }, |
| "id": { |
| "from": "qa_id" |
| }, |
| "question": { |
| "from": "question" |
| }, |
| "answer": { |
| "from": "gt", |
| "optional": true, |
| "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." |
| }, |
| "source": { |
| "format": "huggingface", |
| "url": { |
| "vwtq": "https://huggingface.co/datasets/terryoo/TableVQA-Bench" |
| } |
| } |
| }, |
| "prompt_template_source": { |
| "origin": "official", |
| "reference": "https://github.com/naver-ai/tablevqabench/blob/master/src/example_prompts.py (VWTQ_PROMPT L8-L19)", |
| "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." |
| } |
| }, |
| "vwtq_syn": { |
| "language": [ |
| "en" |
| ], |
| "modalities": [ |
| "single_image_start" |
| ], |
| "task_type": "short_answer_qa", |
| "score_pipeline": [ |
| "exact-match", |
| "rule-match" |
| ], |
| "score_protocol": { |
| "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.", |
| "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." |
| }, |
| "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:", |
| "mapping_from_source": { |
| "media": { |
| "from": "image", |
| "type": "list", |
| "min_items": 1, |
| "max_items": 1 |
| }, |
| "id": { |
| "from": "qa_id" |
| }, |
| "question": { |
| "from": "question" |
| }, |
| "answer": { |
| "from": "gt", |
| "optional": true, |
| "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." |
| }, |
| "source": { |
| "format": "huggingface", |
| "url": { |
| "vwtq_syn": "https://huggingface.co/datasets/terryoo/TableVQA-Bench" |
| } |
| } |
| }, |
| "prompt_template_source": { |
| "origin": "official", |
| "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))", |
| "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." |
| } |
| }, |
| "vtabfact": { |
| "language": [ |
| "en" |
| ], |
| "modalities": [ |
| "single_image_start" |
| ], |
| "task_type": "yes_no_qa", |
| "score_pipeline": [ |
| "exact-match", |
| "rule-match" |
| ], |
| "score_protocol": { |
| "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.", |
| "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." |
| }, |
| "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:", |
| "mapping_from_source": { |
| "media": { |
| "from": "image", |
| "type": "list", |
| "min_items": 1, |
| "max_items": 1 |
| }, |
| "id": { |
| "from": "qa_id" |
| }, |
| "question": { |
| "from": "question" |
| }, |
| "answer": { |
| "from": "gt", |
| "optional": true, |
| "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." |
| }, |
| "source": { |
| "format": "huggingface", |
| "url": { |
| "vtabfact": "https://huggingface.co/datasets/terryoo/TableVQA-Bench" |
| } |
| } |
| }, |
| "prompt_template_source": { |
| "origin": "official", |
| "reference": "https://github.com/naver-ai/tablevqabench/blob/master/src/example_prompts.py (VTABFACT_PROMPT L21-L31)", |
| "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." |
| } |
| }, |
| "fintabnetqa": { |
| "language": [ |
| "en" |
| ], |
| "modalities": [ |
| "single_image_start" |
| ], |
| "task_type": "short_answer_qa", |
| "score_pipeline": [ |
| "exact-match", |
| "rule-match" |
| ], |
| "score_protocol": { |
| "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.", |
| "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." |
| }, |
| "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:", |
| "mapping_from_source": { |
| "media": { |
| "from": "image", |
| "type": "list", |
| "min_items": 1, |
| "max_items": 1 |
| }, |
| "id": { |
| "from": "qa_id" |
| }, |
| "question": { |
| "from": "question" |
| }, |
| "answer": { |
| "from": "gt", |
| "optional": true, |
| "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." |
| }, |
| "source": { |
| "format": "huggingface", |
| "url": { |
| "fintabnetqa": "https://huggingface.co/datasets/terryoo/TableVQA-Bench" |
| } |
| } |
| }, |
| "prompt_template_source": { |
| "origin": "official", |
| "reference": "https://github.com/naver-ai/tablevqabench/blob/master/src/example_prompts.py (FINTABNETQA_PROMPT L33-L46)", |
| "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." |
| } |
| } |
| } |
| } |
