如何利用个人文本知识进行个性化会话信息检索
How to Leverage Personal Textual Knowledge for Personalized Conversational Information Retrieval
AI总结：
本文探索利用大语言模型从个人文本知识库中选取知识进行查询重构，以提升个性化会话信息检索效果，发现单独使用PTKB未必改善结果，但高质量指导下LLM能生成更优查询。
摘要：
个性化会话信息检索（CIR）结合了会话和可个性化元素，通过基于用户背景的多轮交互来满足各类用户的复杂信息需求。其关键在于个人文本知识库（PTKB）能提升CIR效果，因为检索结果会更贴合用户背景。然而，PTKB存在噪声：并非PTKB中的每条知识都与当前特定查询相关。本文探索并测试了利用大语言模型（LLM）从PTKB中选取知识并用于查询重构的多种方法。实验结果表明，单独使用PTKB未必总能改善搜索结果，但在提供高质量指导时，LLM能帮助生成更合适的个性化查询。
英文摘要：
Personalized conversational information retrieval (CIR) combines conversational and personalizable elements to satisfy various users' complex information needs through multi-turn interaction based on their backgrounds. The key promise is that the personal textual knowledge base (PTKB) can improve the CIR effectiveness because the retrieval results can be more related to the user's background. However, PTKB is noisy: not every piece of knowledge in PTKB is relevant to the specific query at hand. In this paper, we explore and test several ways to select knowledge from PTKB and use it for query reformulation by using a large language model (LLM). The experimental results show the PTKB might not always improve the search results when used alone, but LLM can help generate a more appropriate personalized query when high-quality guidance is provided.
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。
