个性化陷阱：用户记忆如何改变大语言模型的情感推理
The Personalization Trap: How User Memory Alters Emotional Reasoning in LLMs
AI总结：
研究用户记忆如何导致大语言模型在情感推理中产生系统性偏差，发现高绩效模型对优势背景用户的情感解读更准确，个性化机制可能嵌入社会等级。
摘要：
当AI助手记住Sarah是一位打两份工的单亲母亲时，它对她压力的解读是否与她是富有的高管时不同？随着个性化AI系统越来越多地融入长期用户记忆，理解这种记忆如何塑造情感推理至关重要。我们通过在人验证的情感智能测试上评估15个模型，研究用户记忆如何影响大语言模型（LLMs）的情感智能。我们发现，相同的场景搭配不同的用户画像会产生系统性不同的情感解读。在经验证的独立于用户的情感场景和多样化的用户画像中，几个高性能LLM出现了系统性偏差，其中优势背景的用户画像获得了更准确的情感解读。此外，LLM在情感推理和支持性推荐任务中表现出跨人口统计因素的显著差异，表明个性化机制可以将社会等级嵌入模型的情感推理中。这些结果凸显了记忆增强AI的一个关键挑战：为个性化设计的系统可能会强化社会不平等。为缓解这些差异，我们整理了一个通用偏好数据集，旨在减少人口统计画像对情感理解的影响。
英文摘要：
When an AI assistant remembers that Sarah is a single mother working two jobs, does it interpret her stress differently than if she were a wealthy executive? As personalized AI systems increasingly incorporate long-term user memory, understanding how this memory shapes emotional reasoning is critical. We investigate how user memory affects emotional intelligence in large language models (LLMs) by evaluating 15 models on human-validated emotional intelligence tests. We find that identical scenarios paired with different user profiles produce systematically divergent emotional interpretations. Across validated user-independent emotional scenarios and diverse user profiles, systematic biases emerged in several high-performing LLMs where advantaged profiles received more accurate emotional interpretations. Moreover, LLMs demonstrate significant disparities across demographic factors in emotion reasoning and supportive recommendations tasks, indicating that personalization mechanisms can embed social hierarchies into models' emotional reasoning. These results highlight a key challenge for memory-enhanced AI: systems designed for personalization may reinforce social inequalities. To mitigate these disparities, we curate a general-purpose preference dataset designed to reduce demographic profiles' influence on emotional understanding.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
