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Evaluating LLMs in Finance Requires Explicit Bias Consideration

Yaxuan Kong, Hoyoung Lee, Yoontae Hwang, Alejandro Lopez-Lira, Bradford Levy, Dhagash Mehta, Qingsong Wen, Chanyeol Choi, Yongjae Lee, Stefan Zohren

TL;DR

This paper argues that finance-specific biases critically distort LLM evaluations and backtests, undermining deployment claims. It identifies five recurring biases—look-ahead, survivorship, narrative, objective, and cost bias—and demonstrates their under-reporting across 164 papers (2023–2025). To address this, it introduces the Structural Validity Framework, comprising Temporal Sanitation, Dynamic Universe Construction, Rationale Robustness, Epistemic Calibration, and Realistic Implementation Constraints, along with a binary pass/fail bias-checklist. A practitioner-oriented user study corroborates the need for standardized bias diagnostics, showing widespread tool scarcity and reliance on noisier, non-deployable evaluations. The framework aims to enable credible, deployment-relevant assessments of financial LLMs by ensuring non-anticipative data usage, inclusive evaluation universes, accountability of explanations, calibrated uncertainty, and realistic cost modeling.

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 https://github.com/Eleanorkong/Awesome-Financial-LLM-Bias-Mitigation.

Evaluating LLMs in Finance Requires Explicit Bias Consideration

TL;DR

This paper argues that finance-specific biases critically distort LLM evaluations and backtests, undermining deployment claims. It identifies five recurring biases—look-ahead, survivorship, narrative, objective, and cost bias—and demonstrates their under-reporting across 164 papers (2023–2025). To address this, it introduces the Structural Validity Framework, comprising Temporal Sanitation, Dynamic Universe Construction, Rationale Robustness, Epistemic Calibration, and Realistic Implementation Constraints, along with a binary pass/fail bias-checklist. A practitioner-oriented user study corroborates the need for standardized bias diagnostics, showing widespread tool scarcity and reliance on noisier, non-deployable evaluations. The framework aims to enable credible, deployment-relevant assessments of financial LLMs by ensuring non-anticipative data usage, inclusive evaluation universes, accountability of explanations, calibrated uncertainty, and realistic cost modeling.

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 https://github.com/Eleanorkong/Awesome-Financial-LLM-Bias-Mitigation.
Paper Structure (29 sections, 3 equations, 11 figures, 1 table)

This paper contains 29 sections, 3 equations, 11 figures, 1 table.

Table of Contents

  1. Introduction
  2. Biases That Are Being Overlooked
  3. Sin #1: Look-Ahead Bias
  4. Sin #2: Survivorship Bias
  5. Sin #3: Narrative Bias
  6. Sin #4: Objective Bias
  7. Sin #5: Cost Bias
  8. The Structural Validity Framework: Guidance for Evaluation
  9. Temporal Sanitation
  10. Dynamic Universe Construction
  11. Rationale Robustness
  12. Epistemic Calibration
  13. Realistic Implementation Constraints
  14. The Need for a Structural Validity Framework: Evidence from a User Study
  15. Alternative Views
  16. ...and 14 more sections

Figures (11)

  • Figure 1: Trends in Financial LLM research (2023–2025). Venue breakdown includes Top ML (ICML, ICLR, NeurIPS), Data Mining (KDD, AAAI, IJCAI, CIKM), NLP (ACL, EMNLP), NAACL, ICAIF (financial ML), and workshops; the line shows annual totals. NAACL is listed separately due to conference scheduling.
  • Figure 2: Bias distribution across 164 LLM-for-Finance papers. Gray bars denote total paper; colored denote mentions per bias.
  • Figure 3: The illusion of validity in Financial LLM evaluation. The figure illustrates five common biases that arise from data construction, model behavior, and deployment assumptions.
  • Figure 4: Overview of the Structural Validity Checklist.
  • Figure 5: Biggest bottlenecks to effective bias mitigation (Q11).
  • ...and 6 more figures