個性化陷阱：用戶記憶如何改變大型語言模型中的情感推理

摘要

當一個人工智慧助手記住莎拉是一位身兼兩份工作的單親母親時，它是否會以不同於對待一位富裕高管的方式來解讀她的壓力？隨著個性化AI系統越來越多地融入長期用戶記憶，理解這種記憶如何塑造情感推理變得至關重要。我們通過在經過人類驗證的情感智力測試上評估15個大型語言模型（LLMs），探討了用戶記憶如何影響這些模型的情感智力。我們發現，相同的場景搭配不同的用戶檔案會產生系統性分歧的情感解讀。在經過驗證的用戶獨立情感場景和多樣化的用戶檔案中，幾個表現優異的LLMs出現了系統性偏見，其中優勢群體的檔案獲得了更為準確的情感解讀。此外，LLMs在情感理解和支持性建議任務中展現出顯著的跨人口統計因素差異，這表明個性化機制可能將社會階層嵌入模型的情感推理之中。這些結果凸顯了記憶增強型AI面臨的一個關鍵挑戰：旨在實現個性化的系統可能無意中加劇了社會不平等。

English

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 understanding 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 inadvertently reinforce social inequalities.

個性化陷阱：用戶記憶如何改變大型語言模型中的情感推理

The Personalization Trap: How User Memory Alters Emotional Reasoning in LLMs

摘要

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