Summary of Findver: Explainable Claim Verification Over Long and Hybrid-content Financial Documents, by Yilun Zhao et al.
FinDVer: Explainable Claim Verification over Long and Hybrid-Content Financial Documents
by Yilun Zhao, Yitao Long, Yuru Jiang, Chengye Wang, Weiyuan Chen, Hongjun Liu, Yiming Zhang, Xiangru Tang, Chen Zhao, Arman Cohan
First submitted to arxiv on: 8 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research introduces FinDVer, a novel benchmark designed to assess the ability of Large Language Models (LLMs) to verify claims and understand long, hybrid-content financial documents. The benchmark consists of 2,400 expert-annotated examples divided into three subsets: information extraction, numerical reasoning, and knowledge-intensive reasoning. The authors evaluate a range of LLMs under different settings, including long-context and RAG settings, and demonstrate that even the best-performing system, GPT-4o, still lags behind human experts. The study provides insights into model reasoning errors and chain-of-thought reasoning, offering valuable guidance for future advancements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research creates a special test to see how well computer models can understand and analyze long financial reports. It’s like a big quiz with 2,400 questions that are carefully checked by experts. The computer models are tested in different ways, including looking at longer texts and trying to figure out more complex problems. Even the best model is still not as good as a human expert. The study also looks at how the models think and makes suggestions for making them better. |
Keywords
» Artificial intelligence » Gpt » Rag
