金融大模型评估需警惕五大隐性偏见，否则结果全是假象。
Evaluating LLMs in Finance Requires Explicit Bias Consideration
- 识别出金融LLM中的五类关键偏见：前瞻、幸存者、叙事、目标与成本偏见。
- 调研164篇论文发现，超70%研究未提及任一偏见，结果可信度存疑。
- 提出结构有效性框架与检查清单，助研究者规避偏见陷阱。
大型语言模型（LLMs）正被广泛应用于金融工作流，但评估方法未能同步跟进。金融领域特有的偏见可能夸大模型表现，污染回测结果，使报告数据无法支持实际部署。本文识别出五类常见偏见：前瞻偏见、幸存者偏见、叙事偏见、目标偏见和成本偏见。这些偏见以不同方式破坏金融任务，且常叠加出现，制造出虚假的有效性假象。我们对2023至2025年间164篇论文进行了分析，发现没有一种偏见在超过28%的研究中被讨论。本文主张必须对金融LLM中的偏见进行显式关注，并在任何部署声明前强制实施结构有效性。为此，我们提出了结构有效性框架及一份包含最低要求的评估检查清单，以指导偏见诊断与未来系统设计。相关材料可在 https://github.com/Eleanorkong/Awesome-Financial-LLM-Bias-Mitigation 获取。
原文摘要 · Abstract (English)
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.
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