BenchPreS：面向持久性内存大语言模型情境感知个性化偏好选择性的基准测试框架

BenchPreS: A Benchmark for Context-Aware Personalized Preference Selectivity of Persistent-Memory LLMs

March 17, 2026

作者: Sangyeon Yoon, Sunkyoung Kim, Hyesoo Hong, Wonje Jeung, Yongil Kim, Wooseok Seo, Heuiyeen Yeen, Albert No

cs.AI

摘要

大型语言模型（LLMs）日益将用户偏好存储于持久记忆中以实现跨交互的个性化服务。然而，在受社会与制度规范约束的第三方沟通场景中，部分用户偏好的应用可能失当。我们提出BenchPreS评估框架，用以检验基于记忆的用户偏好在不同沟通情境中是否得到恰当应用或合理抑制。通过误用率（MR）与恰当应用率（AAR）两项互补指标，研究发现即使是前沿LLMs也难以实现情境敏感的偏好应用。偏好遵循能力更强的模型表现出更高的过度应用倾向，而推理能力与提示词防御策略均未能完全解决该问题。这些结果表明，当前LLMs将个性化偏好视为全局强制规则，而非依情境而变的规范性信号。

English

Large language models (LLMs) increasingly store user preferences in persistent memory to support personalization across interactions. However, in third-party communication settings governed by social and institutional norms, some user preferences may be inappropriate to apply. We introduce BenchPreS, which evaluates whether memory-based user preferences are appropriately applied or suppressed across communication contexts. Using two complementary metrics, Misapplication Rate (MR) and Appropriate Application Rate (AAR), we find even frontier LLMs struggle to apply preferences in a context-sensitive manner. Models with stronger preference adherence exhibit higher rates of over-application, and neither reasoning capability nor prompt-based defenses fully resolve this issue. These results suggest current LLMs treat personalized preferences as globally enforceable rules rather than as context-dependent normative signals.