ExpliCIT-QA: Explainable Code-Based Image Table Question Answering

• URL: http://arxiv.org/abs/2507.11694v1
• Date: Tue, 15 Jul 2025 19:51:24 GMT
• Title: ExpliCIT-QA: Explainable Code-Based Image Table Question Answering
• Authors: Maximiliano Hormazábal Lagos, Álvaro Bueno Sáez, Pedro Alonso Doval, Jorge Alcalde Vesteiro, Héctor Cerezo-Costas,
• Abstract summary: ExpliCIT-QA follows a modular design, consisting of: (1) Multimodal Table Understanding, which uses a Chain-of-Thought 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.<n>This strategy works towards closing the explainability gap in end-to-end Table
• Score: 0.157286095422595
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present ExpliCIT-QA, a system that extends our previous MRT approach for tabular question answering into a multimodal 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-of-Thought 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-to-end TableVQA systems. We evaluated ExpliCIT-QA on the TableVQA-Bench 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.

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