测试大模型在金融场景下的伦理对齐，发现不同模型行为差异大。
Chat Bankman-Fried: an Exploration of LLM Alignment in Finance
- 让12个大模型扮演金融公司CEO，测试其是否愿意挪用客户资产还债。
- 发现模型在风险规避、利润预期等影响下表现出显著的不道德倾向差异。
- 适合关注AI金融安全与监管的从业者及研究者阅读。
大语言模型（LLMs）的发展重新引发人们对人工智能对齐问题的关注——即人工智能目标与人类价值观的一致性。随着各国出台AI安全法规，对齐需在不同领域定义并衡量。本文提出一个实验框架，评估LLMs在相对未被探索的金融领域是否遵守伦理和法律标准。我们让十二个LLMs扮演金融机构首席执行官，测试其在偿还公司债务时挪用客户资产的意愿。从基线配置出发，调整偏好、激励与约束，通过逻辑回归分析各因素影响。结果表明，不同模型在基线层面存在显著的不道德行为倾向差异。风险规避、利润预期与监管环境等因素均按经济理论预测方式影响对齐程度，但影响幅度在不同模型间存在差异。本研究揭示了基于模拟的、事后安全测试的优势与局限：虽能为金融监管机构提供参考，但通用性与成本之间存在明显权衡。
原文摘要 · Abstract (English)
Advancements in large language models (LLMs) have renewed concerns about AI alignment - the consistency between human and AI goals and values. As various jurisdictions enact legislation on AI safety, the concept of alignment must be defined and measured across different domains. This paper proposes an experimental framework to assess whether LLMs adhere to ethical and legal standards in the relatively unexplored context of finance. We prompt twelve LLMs to impersonate the CEO of a financial institution and test their willingness to misuse customer assets to repay outstanding corporate debt. Beginning with a baseline configuration, we adjust preferences, incentives and constraints, analyzing the impact of each adjustment with logistic regression. Our findings reveal significant heterogeneity in the baseline propensity for unethical behavior of LLMs. Factors such as risk aversion, profit expectations, and regulatory environment consistently influence misalignment in ways predicted by economic theory, although the magnitude of these effects varies across LLMs. This paper highlights both the benefits and limitations of simulation-based, ex post safety testing. While it can inform financial authorities and institutions aiming to ensure LLM safety, there is a clear trade-off between generality and cost.
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