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.

“大语言模型正逐渐被用于各种垂直领域,利用其广泛的知识储备来赋能领域中的多种场景。然而,各领域拥有多种待学习的特定任务,且多源异构的领域数据容易引发模型进行任务迁移时的冲突。基于此,本研究提出自适应语义空间学习框架,利用对语义空间内数据的自适应重分布,提升多专家模型的性能及选择效果,并基于此框架训练了一个金融多任务大模型“银瞳”。研究结果表明,我们的框架只需利用10%的数据就能达到接近全数据训练的效果,并拥有较强的泛化表现。”