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ExpliCIT-QA: Explainable Code-Based Image Table Question Answering
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AI
DOI：10.1007/978-3-032-05179-0_26.png)
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Abstract
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We present ExpliCIT-QA, a system that extends our previous MRT approach for tabular question answering into a multi-modal pipeline capable of handling complex table images and providing explainable answers. ExpliCIT-QA follows a modular design, consisting of: (1) Multimodal Table Understanding, which uses a Chain-ofThought approach to extract and transform content from table images; (2) Language-based Reasoning, where a step-by-step explanation in natural language is generated to solve the problem; (3) Automatic Code Generation, where Python/Pandas scripts are created based on the reasoning steps, with feedback for handling errors; (4) Code Execution to compute the final answer; and (5) Natural Language Explanation that describes how the answer was computed. The system is built for transparency and auditability: all intermediate outputs, parsed tables, reasoning steps, generated code, and final answers are available for inspection. This strategy works towards closing the explainability gap in end-toend TableVQA systems. We evaluated ExpliCIT-QA on the TableVQABench benchmark, comparing it with existing baselines. We demonstrated improvements in interpretability and transparency, which open the door for applications in sensitive domains like finance and healthcare where auditing results are critical. Code available at https://github.com/maxhormazabal/ExpliCIT.
Keywords:
Visual Question Answering
Tabular Data
Explainability
Chain-of-Thought Reasoning
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