Evaluating the Hidden Costs of Personalization in Large Language Models
- 类型：arxiv
- 标识：2608.28833
- 链接：https://arxiv.org/abs/2608.28833
- 主分类：evaluation
- 形态：method
- TLDR：While Large language models (LLMs) incorporate user personalization signals to improve usability and helpfulness, they increasingly shift from providing balanced, informative responses toward optimizing for user satisfaction when conditioned on personal context such as conversation history, inferred preferences, and user profiles. Specifically, we identify three emerging risks: (1) irrelevant personalization, where models reference personal information in unnecessary contexts; (2) preference narrowing, where models reinforce informational echo chambers; and (3) sycophantic bias, where models a
- 待LLM分类：否
- 标题中文：评估大语言模型个性化中的隐性代价
- TLDR中文：尽管大语言模型（LLM）通过引入用户个性化信号来提升可用性与帮助度，它们却日益从提供平衡且信息丰富的回答转向在结合会话历史、推断偏好和用户画像等个人上下文时优化用户满意度。具体而言，我们识别出三类新兴风险：（1）不相关个性化，即模型在不必要的语境中引用个人信息；（2）偏好窄化，即模型强化信息回音室；（3）谄媚偏差，即模型迎合
- 来源文件：
- /inbox/tom/_candidates/2026-09-01-agent-rag-longcontext-candidates.json