@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",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T FinDVer: Explainable Claim Verification over Long and Hybrid-content Financial Documents
%A Zhao, Yilun
%A Long, Yitao
%A Jiang, Tintin
%A Wang, Chengye
%A Chen, Weiyuan
%A Liu, Hongjun
%A Tang, Xiangru
%A Zhang, Yiming
%A Zhao, Chen
%A Cohan, Arman
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F zhao-etal-2024-findver
%X 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.
%R 10.18653/v1/2024.emnlp-main.818
%U https://aclanthology.org/2024.emnlp-main.818
%U https://doi.org/10.18653/v1/2024.emnlp-main.818
%P 14739-14752
Markdown (Informal)
[FinDVer: Explainable Claim Verification over Long and Hybrid-content Financial Documents](https://aclanthology.org/2024.emnlp-main.818) (Zhao et al., EMNLP 2024)
ACL
- Yilun Zhao, Yitao Long, Tintin Jiang, Chengye Wang, Weiyuan Chen, Hongjun Liu, Xiangru Tang, Yiming Zhang, Chen Zhao, and Arman Cohan. 2024. FinDVer: Explainable Claim Verification over Long and Hybrid-content Financial Documents. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 14739–14752, Miami, Florida, USA. Association for Computational Linguistics.