@inproceedings{hu-etal-2025-fintrust,
title = "{F}in{T}rust: A Comprehensive Benchmark of Trustworthiness Evaluation in Finance Domain",
author = "Hu, Tiansheng and
Hu, Tongyan and
Bai, Liuyang and
Zhao, Yilun and
Cohan, Arman and
Zhao, Chen",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.512/",
pages = "10110--10139",
ISBN = "979-8-89176-332-6",
abstract = "Recent LLMs have demonstrated promising ability in solving finance related problems. However, applying LLMs in real-world finance application remains challenging due to its high risk and high stakes property. This paper introduces FinTrust, a comprehensive benchmark specifically designed for evaluating the trustworthiness of LLMs in finance applications. Our benchmark focuses on a wide range of alignment issues based on practical context and features fine-grained tasks for each dimension of trustworthiness evaluation. We assess eleven LLMs on FinTrust and find that proprietary models like o4-mini outperforms in most tasks such as safety while open-source models like DeepSeek-V3 have advantage in specific areas like industry-level fairness. For challenging task like fiduciary alignment and disclosure, all LLMs fall short, showing a significant gap in legal awareness. We believe that FinTrust can be a valuable benchmark for LLMs' trustworthiness evaluation in finance domain."
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%0 Conference Proceedings
%T FinTrust: A Comprehensive Benchmark of Trustworthiness Evaluation in Finance Domain
%A Hu, Tiansheng
%A Hu, Tongyan
%A Bai, Liuyang
%A Zhao, Yilun
%A Cohan, Arman
%A Zhao, Chen
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F hu-etal-2025-fintrust
%X Recent LLMs have demonstrated promising ability in solving finance related problems. However, applying LLMs in real-world finance application remains challenging due to its high risk and high stakes property. This paper introduces FinTrust, a comprehensive benchmark specifically designed for evaluating the trustworthiness of LLMs in finance applications. Our benchmark focuses on a wide range of alignment issues based on practical context and features fine-grained tasks for each dimension of trustworthiness evaluation. We assess eleven LLMs on FinTrust and find that proprietary models like o4-mini outperforms in most tasks such as safety while open-source models like DeepSeek-V3 have advantage in specific areas like industry-level fairness. For challenging task like fiduciary alignment and disclosure, all LLMs fall short, showing a significant gap in legal awareness. We believe that FinTrust can be a valuable benchmark for LLMs’ trustworthiness evaluation in finance domain.
%U https://aclanthology.org/2025.emnlp-main.512/
%P 10110-10139
Markdown (Informal)
[FinTrust: A Comprehensive Benchmark of Trustworthiness Evaluation in Finance Domain](https://aclanthology.org/2025.emnlp-main.512/) (Hu et al., EMNLP 2025)
ACL