FinDVer: Explainable Claim Verification over Long and Hybrid-Content Financial Documents

• URL: http://arxiv.org/abs/2411.05764v1
• Date: Fri, 08 Nov 2024 18:26:17 GMT
• Title: FinDVer: Explainable Claim Verification over Long and Hybrid-Content Financial Documents
• Authors: Yilun Zhao, Yitao Long, Yuru Jiang, Chengye Wang, Weiyuan Chen, Hongjun Liu, Yiming Zhang, Xiangru Tang, Chen Zhao, Arman Cohan,
• Abstract summary: FinDVer contains 2,400 expert-annotated examples, divided into three subsets: information extraction, numerical reasoning, and knowledge-intensive reasoning. Our results show that even the current best-performing system, GPT-4o, still lags behind human experts.
• Score: 30.402524394183857
• License:
• Abstract: We introduce FinDVer, a comprehensive benchmark specifically designed to evaluate the explainable claim verification capabilities of LLMs in the context of understanding and analyzing long, hybrid-content financial documents. FinDVer contains 2,400 expert-annotated examples, divided into three subsets: information extraction, numerical reasoning, and knowledge-intensive reasoning, each addressing common scenarios encountered in real-world financial contexts. We assess a broad spectrum of LLMs under long-context and RAG settings. Our results show that even the current best-performing system, GPT-4o, still lags behind human experts. We further provide in-depth analysis on long-context and RAG setting, Chain-of-Thought reasoning, and model reasoning errors, offering insights to drive future advancements. We believe that FinDVer can serve as a valuable benchmark for evaluating LLMs in claim verification over complex, expert-domain documents.

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