In this paper, we establish a benchmark for table visual question answering, referred to as the TableVQA-Bench, derived from pre-existing table question-answering (WTQ , TabFact) and table structure recognition datasets (FinTabNet). It is important to note that existing datasets have not incorporated images or QA pairs, which are two crucial components of TableVQA. As such, the primary objective of this paper is to obtain these necessary components. Specifically, images are sourced either through the application of a stylesheet or by employing the proposed table rendering system. QA pairs are generated by exploiting the large language model (LLM) where the input is a text-formatted table. Ultimately, the completed TableVQA-Bench comprises 1,500 QA pairs. We comprehensively compare the performance of various multi-modal large language models (MLLMs) on TableVQA-Bench. Our findings suggest that processing visual inputs is more challenging than text inputs, as evidenced by the lower performance of MLLMs, despite generally requiring higher computational costs than LLMs.
TableVQA-Bench encompasses VWTQ, VTabFact, and FinTabNetQA, which are extended from pre-existing databases such as WTQ, TabFact, and FinTabNet correspondingly. To acquire images for VWTQ and VTabFact, we source images by attaching the stylesheet of Wikipedia or by utilizing our table rendering system. Conversely, FinTabNet is devoid of the QA pair, which is generated by employing the GPT-4. In the final stage of these processes, any samples with more than 50 table rows are methodically filtered out and the authors carry out a meticulous review.
pip install -r requirements.txt
Example of model prediction: inference_examples.ipynb
Output of inference: outputs/gpt4v
python ./src/evaluate.py --root_input_path [INFERENCE OUTPUT] --output_path [YOUR OUTPUT] --evaluation_datasets vwtq vwtq_syn vtabfact fintabnetqa
Example of evaluation: evaluate_tablevqa.sh
Output of evaluation: outputs/gpt4v_evaluated
If you find this work useful to you, please cite:
@inproceedings{kim2024tablevqabench,
title = {TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domains},
author = {Kim, Yoonsik and Yim, Moonbin and Song, Ka Yeon},
year = {2024}
}TableVQA-Bench
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