How to Leverage Personal Textual Knowledge for Personalized Conversational Information Retrieval
This work addresses improving retrieval effectiveness for users in personalized conversational search, but it is incremental as it builds on existing methods with LLMs.
The study tackled the problem of noisy personal textual knowledge bases (PTKB) in personalized conversational information retrieval (CIR) by testing methods to select relevant knowledge and use it for query reformulation with a large language model (LLM). The results showed that PTKB alone does not always improve search, but LLM can generate more appropriate personalized queries when given high-quality guidance.
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.