发表机构
The International School, Bangalore; Indian Institute of Science (IISc), Bangalore（班加罗尔国际学校; 印度科学理工学院（班加罗尔））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究揭示语言模型在财务信息缺失时，会依据投资者身份（如性别、家庭规模）填补虚构事实，导致建议显著偏移，且身份影响随披露减少而增强，提示需在真实披露水平下审计咨询系统。
AI 中文摘要
人们越来越多地询问大型语言模型如何处理他们的资金，却很少完整描述自己的财务状况。本文探讨模型如何应对这一信息缺口。在保持财务状况不变、仅改变投资者身份声称的情况下，我们将提示中的财务证据从八项事实逐步减少到零项，并衡量推荐股权配置的变动幅度。基于100个财务档案、138种人物设定和七种披露条件构建的96,600个提示（使用Llama-3.1-8B-Instruct模型）中，两个财务状况相同但身份不同的人物设定之间的平均差距，在完全披露时为4.78个百分点，在无财务事实时升至10.34个百分点。对重复提示仅计数一次的双向聚类自助法估计该比值为2.16（95%置信区间1.69至2.79），且仅剩一项事实时该比值已升至1.69倍。身份在完全披露时解释了建议中档案内变异的5%，在无披露时解释了96%。家庭规模是唯一在证据减少时影响可靠增长的属性。一旦标准误差按人物设定（身份分配的单位）进行聚类，会议版本中报告的大多数属性特定交互效应失去显著性，而性别则表现为一个小的持续差距，完全披露也无法消除。仅陈述风险偏好即可使变动幅度达到两至七项一般事实时的水平。在无事实情况下，模型的一行理由引用了从未告知的收入、债务和储蓄，而这些虚构的财务信息对大家庭而言更常呈现不利结果。在网络内部，性别在每一层均可线性解码，且在五层上消融性别方向后，总体身份变动保持不变。基于此类模型构建的咨询系统应在用户实际达到的披露水平下进行审计，并在整个身份空间而非一次一个属性上进行评判。
英文摘要
People increasingly ask large language models what to do with their money, yet seldom describe their finances in full. This paper asks what a model does with the gap. Holding finances fixed and changing only who the investor is said to be, we grade the financial evidence in the prompt from eight facts to none and measure how far the recommended equity allocation moves. Across 96,600 prompts to Llama-3.1-8B-Instruct, built from 100 financial profiles, 138 personas and seven disclosure conditions, the average gap between two personas with identical finances rises from 4.78 percentage points at full disclosure to 10.34 points with no financial facts. A two-way cluster bootstrap counting duplicated prompts once places the ratio at 2.16 (95% interval 1.69 to 2.79), and the rise is already 1.69-fold with a single fact left. Identity explains 5% of within-profile variation in advice at full disclosure and 96% with no disclosure. Household size is the only attribute whose influence grows reliably as evidence is withdrawn. Once standard errors are clustered on the persona, the unit to which identity was assigned, most attribute-specific interactions reported in the conference version lose significance, and gender instead appears as a small standing gap that full disclosure does not close. Stating risk appetite alone brings the swing into the range seen with two to seven generic facts. With no facts, the model's one-line rationale cites incomes, debts and savings it was never told, and these invented finances turn adverse more often for larger households. Inside the network, gender is linearly decodable at every layer, and ablating the gender direction at five layers leaves the aggregate identity swing unchanged. Advisory systems built on such models should be audited at the disclosure levels users actually reach, and judged across the whole identity space rather than one attribute at a time.
Comments49 pages, 17 figures, 12 tables; Submitted & Accepted to ICAIF'2026
