根据用户打字和提问内容，动态调整AI回答的深度与术语。
ExPerT: Personalizing LLM Responses to Users' Domain Expertise via Query-Wise Semantic and Keystroke Behavioral Cues
- 结合提问文本和打字节奏，实时判断用户专业水平。
- 专家级回答更深入，新手回答更通俗，准确率提升65.7%。
- 适合需要个性化响应的教育、医疗等专业场景。
大型语言模型（LLMs）在终端用户中的应用日益广泛，但现有个性化方法依赖静态资料或仅文本信号，难以捕捉查询时的专业水平差异。本文提出ExPerT，一种基于查询的个性化框架，通过融合语义与行为线索，动态适应用户在特定查询中的领域专业度。该框架包含两个核心模块：(i) 语义-行为专业知识推断模块，利用上下文提示（in-context LLM prompting）联合分析查询文本与打字动态；(ii) 专业知识条件响应生成模块，根据推断结果调节回答的细节程度、术语使用及概念复杂度。对40名参与者、1270次查询进行用户研究发现，与最强基线相比，ExPerT将专业知识推断误差降低65.7%（平均绝对误差MAE从1.162降至0.398），并使回答满意度提升17.52%（从3.71升至4.36，5分制量表）。
原文摘要 · Abstract (English)
Large language models (LLMs) are increasingly used by end users, yet existing personalization methods relying on static profiles or text-only signals fail to capture query-specific expertise variation. We present ExPerT, a query-wise personalization framework that adapts LLM responses to users' query domain expertise by combining semantic and behavioral cues. ExPerT consists of two key components: (i) a semantic-behavioral expertise inference module that jointly interprets query text and keystroke dynamics via in-context LLM prompting, and (ii) an expertise-conditioned response generation that adapts the level of detail, terminology, and conceptual complexity. Our user study with 40 participants and 1270 queries demonstrated that ExPerT reduced expertise inference error by 65.7% compared to the strongest baseline (MAE = 0.398 vs. 1.162) and improved response satisfaction by 17.52% (from 3.71 to 4.36) on a 5-point Likert scale.
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