针对投资者决策的个性化大模型，揭示了现有定制范式的四大局限。
High-Stakes Personalization: Rethinking LLM Customization for Individual Investor Decision-Making
- 构建金融投资场景下的个性化大模型系统，识别四类核心挑战。
- 发现投资者行为随时间演变且自相矛盾，影响投资决策有效性。
- 适合关注高风险长期决策、个性化AI系统的研究者与从业者。
个性化大模型发展迅速，但多数应用在用户偏好稳定、真实标准缺失或主观性强的领域。我们指出，个人投资者决策是大模型个性化中极具挑战的场景，暴露了当前定制范式的根本缺陷。基于为智能组合管理构建并部署的系统，我们识别出四个关键维度：（1）行为记忆复杂性——投资者模式具有时序演化、自我矛盾且财务后果重大；（2）观点一致性漂移——在数周至数月内保持投资逻辑连贯，对无状态和会话有界架构构成压力；（3）风格-信号张力——系统需兼顾个人投资哲学与可能相悖的客观证据；（4）无真实标签对齐——因结果随机且延迟，无法用固定标签评估个性化质量。本文描述了系统构建中产生的架构应对策略，并提出高风险、长周期决策场景下个性化NLP的开放研究方向。
原文摘要 · Abstract (English)
Personalized LLM systems have advanced rapidly, yet most operate in domains where user preferences are stable and ground truth is either absent or subjective. We argue that individual investor decision-making presents a uniquely challenging domain for LLM personalization - one that exposes fundamental limitations in current customization paradigms. Drawing on our system, built and deployed for AI-augmented portfolio management, we identify four axes along which individual investing exposes fundamental limitations in standard LLM customization: (1) behavioral memory complexity, where investor patterns are temporally evolving, self-contradictory, and financially consequential; (2) thesis consistency under drift, where maintaining coherent investment rationale over weeks or months strains stateless and session-bounded architectures; (3) style-signal tension, where the system must simultaneously respect personal investment philosophy and surface objective evidence that may contradict it; and (4) alignment without ground truth, where personalization quality cannot be evaluated against a fixed label set because outcomes are stochastic and delayed. We describe the architectural responses that emerged from building the system and propose open research directions for personalized NLP in high-stakes, temporally extended decision domains.
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