大语言模型能否识别你的潜在偏好？一个面向个性化交互中潜在信息发现的基准
Do LLMs Recognize Your Latent Preferences? A Benchmark for Latent Information Discovery in Personalized Interaction
AI总结：
本文提出面向多轮个性化交互的潜在信息发现基准，通过三类任务和三智能体框架评估LLM揭示隐藏用户偏好的能力，发现其表现因情境在32%至98%间显著波动。
摘要：
大语言模型（LLM）擅长生成大体相关的文本，但当需要用户特定偏好时，例如推荐餐厅或规划旅行，这种通用性便成为局限。在这些场景中，用户很少明确说出每项偏好；相反，他们在意的许多内容仍是潜在的，等待被推断。这引出一个根本问题：LLM能否通过对话发现并推理此类潜在信息？我们通过引入一个统一基准来解决该问题，用于评估潜在信息发现，即LLM通过多轮交互揭示并利用隐藏用户属性的能力。该基准涵盖三种逐渐贴近现实的设置：经典的20 Questions游戏、个性化问答和个性化文本摘要。所有任务共享一个三智能体框架（User、Assistant、Judge），可对信息引导与适应进行回合级评估。结果表明，尽管LLM确实能够通过对话呈现潜在信息，但其成功率随情境差异巨大：从32%到98%不等，具体取决于任务复杂性、主题以及隐藏属性数量。该基准为研究个性化交互中的潜在信息发现提供了首个系统性框架，并强调有效的偏好推断仍是构建真正自适应AI系统的开放前沿。
英文摘要：
Large Language Models (LLMs) excel at producing broadly relevant text, but this generality becomes a limitation when user-specific preferences are required, such as recommending restaurants or planning travel. In these scenarios, users rarely articulate every preference explicitly; instead, much of what they care about remains latent, waiting to be inferred. This raises a fundamental question: Can LLMs uncover and reason about such latent information through conversation? We address this problem by introducing a unified benchmark for evaluating latent information discovery - the ability of LLMs to reveal and utilize hidden user attributes through multi-turn interaction. The benchmark spans three progressively realistic settings: the classic 20 Questions game, Personalized Question Answering, and Personalized Text Summarization. All tasks share a tri-agent framework (User, Assistant, Judge) enabling turn-level evaluation of elicitation and adaptation. Our results reveal that while LLMs can indeed surface latent information through dialogue, their success varies dramatically with context: from 32% to 98%, depending on task complexity, topic, and number of hidden attributes. This benchmark provides the first systematic framework for studying latent information discovery in personalized interaction, highlighting that effective preference inference remains an open frontier for building truly adaptive AI 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。
