ChartCitor: Multi-Agent Framework for Fine-Grained Chart Visual Attribution

• URL: http://arxiv.org/abs/2502.00989v1
• Date: Mon, 03 Feb 2025 02:00:51 GMT
• Title: ChartCitor: Multi-Agent Framework for Fine-Grained Chart Visual Attribution
• Authors: Kanika Goswami, Puneet Mathur, Ryan Rossi, Franck Dernoncourt,
• Abstract summary: We present ChartCitor, a multi-agent framework that provides fine-grained bounding box citations by identifying supporting evidence within chart images.<n>The system orchestrates LLM agents to perform chart-to-table extraction, answer reformulation, table augmentation, evidence retrieval through pre-filtering and re-ranking, and table-to-chart mapping.
• Score: 47.79080056618323
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) can perform chart question-answering tasks but often generate unverified hallucinated responses. Existing answer attribution methods struggle to ground responses in source charts due to limited visual-semantic context, complex visual-text alignment requirements, and difficulties in bounding box prediction across complex layouts. We present ChartCitor, a multi-agent framework that provides fine-grained bounding box citations by identifying supporting evidence within chart images. The system orchestrates LLM agents to perform chart-to-table extraction, answer reformulation, table augmentation, evidence retrieval through pre-filtering and re-ranking, and table-to-chart mapping. ChartCitor outperforms existing baselines across different chart types. Qualitative user studies show that ChartCitor helps increase user trust in Generative AI by providing enhanced explainability for LLM-assisted chart QA and enables professionals to be more productive.

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