@inproceedings{zhou-etal-2025-llms,
title = "Are {LLM}s Rational Investors? A Study on the Financial Bias in {LLM}s",
author = "Zhou, Yuhang and
Ni, Yuchen and
Xi, Zhiheng and
Yin, Zhangyue and
He, Yu and
Yunhui, Gan and
Liu, Xiang and
Jian, Zhang and
Liu, Sen and
Qiu, Xipeng and
Cao, Yixin and
Ye, Guangnan and
Chai, Hongfeng",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.1239/",
doi = "10.18653/v1/2025.findings-acl.1239",
pages = "24139--24173",
ISBN = "979-8-89176-256-5",
abstract = "Large language models (LLMs) excel in natural language generation but also exhibit biases, particularly in gender, race, and religion, which can be amplified with widespread use. However, research on biases in specific domains, such as finance, remains limited. To address this gap, we conducted a comprehensive evaluation of 23 leading LLMs and found varying degrees of financial bias, including more pronounced biases in financial-specific LLMs (FinLLMs). In response, we propose the Financial Bias Indicators (FBI) framework, which includes components like the Bias Unveiler, Bias Detective, Bias Tracker, and Bias Antidote, designed to identify, detect, analyze, and mitigate financial biases. Our analysis explores the root causes of these biases and introduces a debiasing method based on financial causal knowledge, alongside three other debiasing techniques. For the most biased model, we successfully reduced bias by 68{\%} according to key metrics. This study advances our understanding of LLM biases in finance and highlights the need for greater scrutiny in their application within this critical domain."
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<abstract>Large language models (LLMs) excel in natural language generation but also exhibit biases, particularly in gender, race, and religion, which can be amplified with widespread use. However, research on biases in specific domains, such as finance, remains limited. To address this gap, we conducted a comprehensive evaluation of 23 leading LLMs and found varying degrees of financial bias, including more pronounced biases in financial-specific LLMs (FinLLMs). In response, we propose the Financial Bias Indicators (FBI) framework, which includes components like the Bias Unveiler, Bias Detective, Bias Tracker, and Bias Antidote, designed to identify, detect, analyze, and mitigate financial biases. Our analysis explores the root causes of these biases and introduces a debiasing method based on financial causal knowledge, alongside three other debiasing techniques. For the most biased model, we successfully reduced bias by 68% according to key metrics. This study advances our understanding of LLM biases in finance and highlights the need for greater scrutiny in their application within this critical domain.</abstract>
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%0 Conference Proceedings
%T Are LLMs Rational Investors? A Study on the Financial Bias in LLMs
%A Zhou, Yuhang
%A Ni, Yuchen
%A Xi, Zhiheng
%A Yin, Zhangyue
%A He, Yu
%A Yunhui, Gan
%A Liu, Xiang
%A Jian, Zhang
%A Liu, Sen
%A Qiu, Xipeng
%A Cao, Yixin
%A Ye, Guangnan
%A Chai, Hongfeng
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F zhou-etal-2025-llms
%X Large language models (LLMs) excel in natural language generation but also exhibit biases, particularly in gender, race, and religion, which can be amplified with widespread use. However, research on biases in specific domains, such as finance, remains limited. To address this gap, we conducted a comprehensive evaluation of 23 leading LLMs and found varying degrees of financial bias, including more pronounced biases in financial-specific LLMs (FinLLMs). In response, we propose the Financial Bias Indicators (FBI) framework, which includes components like the Bias Unveiler, Bias Detective, Bias Tracker, and Bias Antidote, designed to identify, detect, analyze, and mitigate financial biases. Our analysis explores the root causes of these biases and introduces a debiasing method based on financial causal knowledge, alongside three other debiasing techniques. For the most biased model, we successfully reduced bias by 68% according to key metrics. This study advances our understanding of LLM biases in finance and highlights the need for greater scrutiny in their application within this critical domain.
%R 10.18653/v1/2025.findings-acl.1239
%U https://aclanthology.org/2025.findings-acl.1239/
%U https://doi.org/10.18653/v1/2025.findings-acl.1239
%P 24139-24173
Markdown (Informal)
[Are LLMs Rational Investors? A Study on the Financial Bias in LLMs](https://aclanthology.org/2025.findings-acl.1239/) (Zhou et al., Findings 2025)
ACL
- Yuhang Zhou, Yuchen Ni, Zhiheng Xi, Zhangyue Yin, Yu He, Gan Yunhui, Xiang Liu, Zhang Jian, Sen Liu, Xipeng Qiu, Yixin Cao, Guangnan Ye, and Hongfeng Chai. 2025. Are LLMs Rational Investors? A Study on the Financial Bias in LLMs. In Findings of the Association for Computational Linguistics: ACL 2025, pages 24139–24173, Vienna, Austria. Association for Computational Linguistics.