BESPOKE：基于诊断反馈的搜索增强型大语言模型个性化基准测试

BESPOKE: Benchmark for Search-Augmented Large Language Model Personalization via Diagnostic Feedback

September 25, 2025

作者: Hyunseo Kim, Sangam Lee, Kwangwook Seo, Dongha Lee

cs.AI

摘要

搜索增强型大语言模型（LLMs）通过将检索整合到生成过程中，在信息查询任务上取得了显著进展，相比传统搜索系统，减轻了用户的认知负担。然而，它们仍不足以全面满足多样化的用户需求，这需要识别同一查询如何反映不同用户的意图，并以用户偏好的形式提供信息。尽管近期系统如ChatGPT和Gemini尝试通过利用用户历史记录来实现个性化，但对此类个性化的系统性评估仍显不足。为填补这一空白，我们提出了BESPOKE，一个用于评估搜索增强型LLMs个性化能力的真实基准。BESPOKE旨在既真实又具诊断性，通过直接从人类收集真实的聊天和搜索历史，并将响应与细粒度的偏好评分和反馈配对来实现。该基准通过长期、深度参与的人工标注构建，其中人类标注者贡献了自己的历史记录，撰写了包含详细信息需求的查询，并通过评分和诊断反馈评估了响应。利用BESPOKE，我们进行了系统性分析，揭示了信息查询任务中有效个性化的关键要求，为个性化搜索增强型LLMs的细粒度评估奠定了基础。我们的代码和数据可在https://augustinlib.github.io/BESPOKE/获取。

English

Search-augmented large language models (LLMs) have advanced information-seeking tasks by integrating retrieval into generation, reducing users' cognitive burden compared to traditional search systems. Yet they remain insufficient for fully addressing diverse user needs, which requires recognizing how the same query can reflect different intents across users and delivering information in preferred forms. While recent systems such as ChatGPT and Gemini attempt personalization by leveraging user histories, systematic evaluation of such personalization is under-explored. To address this gap, we propose BESPOKE, the realistic benchmark for evaluating personalization in search-augmented LLMs. BESPOKE is designed to be both realistic, by collecting authentic chat and search histories directly from humans, and diagnostic, by pairing responses with fine-grained preference scores and feedback. The benchmark is constructed through long-term, deeply engaged human annotation, where human annotators contributed their own histories, authored queries with detailed information needs, and evaluated responses with scores and diagnostic feedback. Leveraging BESPOKE, we conduct systematic analyses that reveal key requirements for effective personalization in information-seeking tasks, providing a foundation for fine-grained evaluation of personalized search-augmented LLMs. Our code and data are available at https://augustinlib.github.io/BESPOKE/.