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Computer Science > Computation and Language

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By Puneet Mathur, Nedim Lipka, Varun Manjunatha
| arxiv.org
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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Paper page - ChartCitor: Multi-Agent Framework for Fine-Grained Chart Visual Attribution

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By Kanika Goswami Verified, Puneet Mathur, Ryan Rossi, Franck Dernoncourt
| huggingface.co
Papers Published on Feb 3 · Submitted by Franck-Dernoncourt on Feb 7 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.
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PlotGen: Multi-Agent LLM-based Scientific Data Visualization via Multimodal Feedback

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By Puneet Mathur, Ryan Rossi, Franck Dernoncourt
| arxiv.org
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