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TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domains

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arxiv 2404.19205 v1 pith:UUMINGEW submitted 2024-04-30 cs.CV cs.AI

Yoonsik Kim , Moonbin Yim , Ka Yeon Song This is my paper
classification cs.CVcs.AI
keywords tablemllmstablevqa-benchpairsperformancevisualansweringbenchmark
verification ladder T0 review T1 audit T2 compute T3 formal
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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 (QA) and table structure recognition datasets. 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 \textit{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. GPT-4V achieves the highest accuracy among commercial and open-sourced MLLMs from our experiments. Moreover, we discover that the number of vision queries plays a significant role in TableVQA performance. To further analyze the capabilities of MLLMs in comparison to their LLM backbones, we investigate by presenting image-formatted tables to MLLMs and text-formatted tables to LLMs, respectively. 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. The proposed TableVQA-Bench and evaluation codes are available at \href{https://github.com/naver-ai/tablevqabench}{https://github.com/naver-ai/tablevqabench}.

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Cited by 10 Pith papers

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    VLM accuracy can be predicted from a scalar capability score derived from LLM text benchmarks plus multimodal data volume via a fitted transfer-absorption scaling law.

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