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About this paper
Guo Yutong, Wanying Wang, Yue Wu, Zichen Miao, Haoyu Wang
The TALENT framework introduces an efficient and accurate approach for Table Visual Question Answering by integrating a lightweight Visual-Language Model as a perception-narration module with a Large Language Model for reasoning. It leverages a dual representation of tables, combining precise OCR spans with rich natural language narration, to achieve 81.13% accuracy on TableVQA-Bench and demonstrate robust performance on multi-step quantitative reasoning tasks.
Key insights
- Combining symbolic OCR with rich natural language narration (a dual representation) is crucial for robust Table VQA, effectively addressing both precision and contextual understanding challenges.
- An LLM-centric architecture, where a VLM preprocesses visual information into LLM-amenable formats, provides a more efficient and accurate paradigm for multimodal reasoning compared to monolithic VLMs.
- The reasoning capacity of the LLM component is approximately three times more impactful on overall performance than the VLM's size, once sufficient visual information is extracted, guiding efficient model scaling.
Summary by alphaXiv.
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