BenchPreS: A Benchmark for Context-Aware Personalized Preference Selectivity of Persistent-Memory LLMs
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.
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Sangyeon Yoon
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2026-03-19T13:01:41.412Z
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yoon
sangyon
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sangyon
69ba67605d2b9c28ea09f30f
Sunkyoung Kim
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2026-03-19T13:01:43.495Z
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Sunkyoung Kim
Sunkyoung
user
Sunkyoung
69ba67605d2b9c28ea09f310
Hyesoo Hong
69ba67605d2b9c28ea09f311
Wonje Jeung
69ba67605d2b9c28ea09f312
Yongil Kim
69ba67605d2b9c28ea09f313
Wooseok Seo
69ba67605d2b9c28ea09f314
Heuiyeen Yeen
69ba67605d2b9c28ea09f315
Albert No
BenchPreS: A Benchmark for Context-Aware Personalized Preference Selectivity of Persistent-Memory LLMs
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.
Large language models struggle to appropriately apply user preferences in context-sensitive communication settings, treating personalized preferences as universal rules rather than normative signals.
large language models user preferences persistent memory personalization communication contexts BenchPreS Misapplication Rate Appropriate Application Rate