FinDVer: Explainable Claim Verification over Long and Hybrid-content Financial Documents
Yilun Zhao, Yitao Long, Tintin Jiang, Chengye Wang, Weiyuan Chen, Hongjun Liu, Xiangru Tang, Yiming Zhang
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 4,000 expert-annotated examples across four subsets, each focusing on a type of scenario that frequently arises in real-world financial domains. We assess a broad spectrum of 25 LLMs under long-context and RAG settings. Our results show that even the current best-performing system (i.e., GPT-4o) significantly lags behind human experts. Our detailed findings and insights highlight the strengths and limitations of existing LLMs in this new task. We believe FinDVer can serve as a valuable benchmark for evaluating LLM capabilities in claim verification over complex, expert-domain documents.
BibTeX
@inproceedings{zhao-etal-2024-findver,
title = "{F}in{DV}er: Explainable Claim Verification over Long and Hybrid-content Financial Documents",
author = "Zhao, Yilun and
Long, Yitao and
Jiang, Tintin and
Wang, Chengye and
Chen, Weiyuan and
Liu, Hongjun and
Tang, Xiangru and
Zhang, Yiming and
Zhao, Chen and
Cohan, Arman",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.818/",
doi = "10.18653/v1/2024.emnlp-main.818",
pages = "14739--14752"
}