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Chart understanding is crucial for applying Multimodal Large Language Models (MLLMs) to tasks like analyzing scientific papers and financial reports. However, current datasets often use simplified cha
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2026-02-18 03:50:56
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CharXiv CharXiv Charting Gaps in Realistic Chart Understanding in Multimodal LLMs Zirui Wang 1 , Mengzhou Xia 1 , Luxi He 1 , Howard Chen 1 , Yitao Liu 3 , Richard Zhu 1 , Kaiqu Liang 1 , Xindi Wu 1 , Haotian Liu 2 , Sadhika Malladi 1 , Alexis Chevalier 1 , Sanjeev Arora 1 , Danqi Chen 1 1 Princeton Language and Intelligence (PLI), Princeton University 2 University of Wisconsin, Madison 3 The University of Hong Kong NeurIPS 2024 arXiv Code Data Leaderboard Watch the 80-second music video to learn the motivation and key findings of CharXiv! (Lyrics by GPT-4o from the abstract and Music by Suno ) Introduction Chart understanding plays a pivotal role when applying Multimodal Large Language Models (MLLMs) to real-world tasks such as analyzing scientific papers or financial reports. However, existing datasets often focus on oversimplified and homogeneous charts...
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