行为特质泄漏进偏好：诊断LLM用户模拟器中的特质干扰
A Behavioral Trait Leaks into Preferences: Diagnosing Trait Interference in LLM User Simulators
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中文总结 AI 辅助
本研究针对LLM用户模拟器中行为特质干扰偏好导致评估失效的问题，提出页面级质量锚定方法PQA，通过个性化锚点引导模拟器主动退出低质量页面，从而缓解特质干扰并提升评估可靠性。
中文摘要 AI 辅助
基于LLM的用户模拟器旨在通过注入特质来模拟用户，从而弥合推荐评估中的离线与在线差距，其中偏好属性决定用户参与的内容，行为活动特质控制其浏览时长。然而，我们表明这种预期的特质独立性在模拟过程中会崩溃，导致两种失败：（i）特质干扰，即增强的活动扭曲偏好边界，并迫使用户与不匹配的条目互动以维持浏览；（ii）评估无效性，即尽管品味不匹配，满意度分数会因活动驱动的页面数而膨胀，使评估偏向于特质分布而非推荐性能。为解决此问题，我们提出PQA，一种页面级质量锚定方法，通过反映每个用户内在偏好标准的个性化锚点来引导模拟器。通过在进一步浏览前评估页面是否达到该标准，PQA能够主动退出低质量页面，使活动特质保留其预期作用，即在符合偏好的页面内调节浏览深度。实验表明，PQA缓解了特质干扰，并提高了基于LLM的模拟器在活动变化下的评估可靠性。我们的代码可在以下网址获取：此https URL
英文摘要
LLM-based user simulators aim to bridge the offline-online gap in recommender evaluation by emulating users through injected traits, where preference attributes determine what a user engages with and a behavioral activity trait governs how long they browse. However, we show this intended trait independence collapses during simulation, causing two failures: (i) Trait Interference, where amplified activity distorts preference boundaries and forces interactions with mismatched items to sustain browsing, and (ii) Evaluation Invalidity, where satisfaction scores inflate with activity-driven page counts despite taste mismatches, biasing evaluation toward trait distributions rather than recommender performance. To resolve this, we propose PQA, a page-level quality anchoring method that guides simulators using a personalized anchor reflecting each user's intrinsic preference standard. By assessing whether a page meets this standard before further browsing, PQA enables proactive exits from low-quality pages, letting the activity trait retain its intended role of modulating browsing depth within preference-conforming pages. Experiments show PQA mitigates trait interference and improves the reliability of LLM-based simulator evaluation under activity shifts. Our code is available at https://github.com/chaehyun1/PQA
发表机构
- KAIST（韩国科学技术院）
机构由 AI 辅助整理，请以论文原文为准。
