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[2602.14233] Evaluating LLMs in Finance Requires Explicit Bias Consideration
Summary
This paper discusses the need for explicit bias consideration in evaluating Large Language Models (LLMs) used in finance, identifying five key biases that can distort performance assessments.
Why It Matters
As LLMs become integral to financial decision-making, understanding and mitigating biases is crucial for ensuring the reliability of their outputs. This paper highlights the gap in current evaluation practices and proposes a framework to enhance structural validity, which is essential for accurate financial modeling and deployment.
Key Takeaways
- Five key biases in financial LLM applications can distort evaluations.
- Current literature shows insufficient discussion of these biases.
- A Structural Validity Framework is proposed to enhance evaluation practices.
- Explicit bias consideration is necessary before deploying LLMs in finance.
- The paper includes an evaluation checklist for bias diagnosis.
Computer Science > Machine Learning arXiv:2602.14233 (cs) [Submitted on 15 Feb 2026] Title:Evaluating LLMs in Finance Requires Explicit Bias Consideration Authors:Yaxuan Kong, Hoyoung Lee, Yoontae Hwang, Alejandro Lopez-Lira, Bradford Levy, Dhagash Mehta, Qingsong Wen, Chanyeol Choi, Yongjae Lee, Stefan Zohren View a PDF of the paper titled Evaluating LLMs in Finance Requires Explicit Bias Consideration, by Yaxuan Kong and 9 other authors View PDF HTML (experimental) Abstract:Large Language Models (LLMs) are increasingly integrated into financial workflows, but evaluation practice has not kept up. Finance-specific biases can inflate performance, contaminate backtests, and make reported results useless for any deployment claim. We identify five recurring biases in financial LLM applications. They include look-ahead bias, survivorship bias, narrative bias, objective bias, and cost bias. These biases break financial tasks in distinct ways and they often compound to create an illusion of validity. We reviewed 164 papers from 2023 to 2025 and found that no single bias is discussed in more than 28 percent of studies. This position paper argues that bias in financial LLM systems requires explicit attention and that structural validity should be enforced before any result is used to support a deployment claim. We propose a Structural Validity Framework and an evaluation checklist with minimal requirements for bias diagnosis and future system design. The material is available at th...