OP-Bench：用于内存增强个性化对话代理的过个性化基准测试
OP-Bench: Benchmarking Over-Personalization for Memory-Augmented Personalized Conversational Agents
AI总结：
OP-Bench通过评估多种模型和方法，揭示了内存增强对话代理中过个性化问题的普遍性，并提出Self-ReCheck机制以缓解该问题。
摘要：
基于长期用户记忆的内存增强对话代理能够实现个性化交互，已获得广泛关注。然而，现有基准测试主要关注代理是否能够回忆并应用用户信息，而忽视了这种个性化是否被适当使用。事实上，代理可能会过度使用个人信息，产生让用户感觉被迫、侵入或不恰当的回复。我们将这一问题称为"过个性化"。在本工作中，我们将过个性化正式分为三种类型：不相关性、重复性和趋炎附势，并引入OP-Bench，一个由长期对话历史构建的1700个验证实例组成的基准测试。使用OP-Bench，我们评估了多种大型语言模型和内存增强方法，并发现引入内存时过个性化普遍存在。进一步分析表明，即使在不需要的情况下，代理也倾向于检索并过度关注用户记忆。为了解决这一问题，我们提出Self-ReCheck，一种轻量、模型无关的记忆过滤机制，能够在不损害个性化性能的情况下缓解过个性化问题。我们的工作在内存增强对话系统中更可控和恰当的个性化方面迈出了第一步。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
