[Submitted on 17 Mar 2026 (v1), last revised 14 Oct 2026 (this version, v2)]
Abstract: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.
Submission history
From: Sangyeon Yoon [view email]
[v1] Tue, 17 Mar 2026 14:19:05 UTC (2,556 KB)
[v2] Wed, 14 Oct 2026 04:23:03 UTC (2,557 KB)
