提出评估对话机器人过度个性化的新基准，发现常见但被忽视的社交不适问题。
OP-Bench: Benchmarking Over-Personalization for Memory-Augmented Personalized Conversational Agents
- 定义三种过度个性化类型：无关、重复、阿谀奉承。
- 在1700条对话中发现引入记忆后过度使用用户信息现象普遍。
- 提出轻量级过滤机制Self-ReCheck，平衡个性化与自然度。
带有记忆的对话代理通过长期用户记忆实现个性化交互，但现有基准仅关注能否回忆和应用用户信息，忽视了个性化是否恰当。事实上，代理可能过度使用个人信息，导致回应生硬、侵扰或社交不当，这种现象称为“过度个性化”。本文将过度个性化形式化为三类：无关性、重复性与阿谀性，并构建了包含1700个经验证实例的OP-Bench基准，基于长期对话历史。利用该基准评估多个大语言模型与记忆增强方法，发现引入记忆后过度个性化现象广泛存在。进一步分析表明，即使无需记忆，代理仍倾向于检索并过度关注用户记忆。为此，本文提出Self-ReCheck——一种轻量级、模型无关的记忆过滤机制，能在保持个性化性能的同时缓解过度个性化问题。本工作为实现更可控、更适宜的个性化对话系统迈出初步一步。
原文摘要 · Abstract (English)
Memory-augmented conversational agents enable personalized interactions using long-term user memory and have gained substantial traction. However, existing benchmarks primarily focus on whether agents can recall and apply user information, while overlooking whether such personalization is used appropriately. In fact, agents may overuse personal information, producing responses that feel forced, intrusive, or socially inappropriate to users. We refer to this issue as \emph{over-personalization}. In this work, we formalize over-personalization into three types: Irrelevance, Repetition, and Sycophancy, and introduce \textbf{OP-Bench} a benchmark of 1,700 verified instances constructed from long-horizon dialogue histories. Using \textbf{OP-Bench}, we evaluate multiple large language models and memory-augmentation methods, and find that over-personalization is widespread when memory is introduced. Further analysis reveals that agents tend to retrieve and over-attend to user memories even when unnecessary. To address this issue, we propose \textbf{Self-ReCheck}, a lightweight, model-agnostic memory filtering mechanism that mitigates over-personalization while preserving personalization performance. Our work takes an initial step toward more controllable and appropriate personalization in memory-augmented dialogue systems.
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