BESPOKE：通过诊断反馈进行搜索增强型大语言模型个性化的基准测试
BESPOKE: Benchmark for Search-Augmented Large Language Model Personalization via Diagnostic Feedback
AI总结：
提出BESPOKE基准，通过收集真实用户历史并配对细粒度偏好分数与反馈，系统评估搜索增强型大语言模型在个性化信息检索任务中的表现。
摘要：
搜索增强型大语言模型（LLMs）通过将检索集成到生成中，推进了信息寻求任务，相比传统搜索系统减少了用户的认知负担。然而，它们在充分满足多样化用户需求方面仍显不足，这需要识别同一查询如何反映不同用户的意图，并以偏好的形式传递信息。尽管最近如ChatGPT和Gemini等系统通过利用用户历史尝试个性化，但对这种个性化的系统评估尚不充分。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个性化的现实基准。BESPOKE的设计既现实（通过直接从人类收集真实的聊天和搜索历史）又具有诊断性（通过将响应与细粒度的偏好分数和反馈配对）。该基准通过长期、深度参与的人工标注构建，标注者贡献自己的历史、撰写带有详细信息需求的查询，并用分数和诊断反馈评估响应。利用BESPOKE，我们进行了系统分析，揭示了信息寻求任务中有效个性化的关键要求，为个性化搜索增强型LLMs的细粒度评估提供了基础。我们的代码和数据可在https://augustinlib.github.io/BESPOKE/获取。
英文摘要：
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/.
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。
