Ask Optimal Questions: Aligning Large Language Models with Retriever’s Preference in Conversation
Chanwoong Yoon, Gangwoo Kim, Byeongguk Jeon, Sungdong Kim, Yohan Jo, Jaewoo Kang · 2025
Conversational search, unlike single-turn retrieval tasks, requires understanding the current question within a dialogue context.The common approach of rewrite-then-retrieve aims to decontextualize questions to be self-sufficient for off-the-shelf retrievers, but most existing methods produce sub-optimal query rewrites due to the limited ability to incorporate signals from the retrieval results.To overcome this limitation, we present a novel framework RETPO (Retriever's Preference Optimization), which is designed to optimize a language model (LM) for reformulating search queries in line with the preferences of the target retrieval systems.The process begins by prompting a large LM to produce various potential rewrites and then collects retrieval performance for these rewrites as the retrievers' preferences.Through the process, we construct a large-scale dataset called RF COLLECTION, containing Retrievers' Feedback on over 410K query rewrites across 12K conversations.Furthermore, we fine-tune a smaller LM on this dataset to align it with the retrievers' feedback.Our resulting model demonstrates superiority on two benchmarks, surpassing the previous state-of-the-art performance of rewrite-then-retrieve approaches. 1