Ask Optimal Questions: Aligning Large Language Models with Retriever's Preference in Conversational Search

• URL: http://arxiv.org/abs/2402.11827v1
• Date: Mon, 19 Feb 2024 04:41:31 GMT
• Title: Ask Optimal Questions: Aligning Large Language Models with Retriever's Preference in Conversational Search
• Authors: Chanwoong Yoon, Gangwoo Kim, Byeongguk Jeon, Sungdong Kim, Yohan Jo, Jaewoo Kang
• Abstract summary: RetPO is designed to optimize a language model (LM) for reformulating search queries in line with the preferences of the target retrieval systems. We construct a large-scale dataset called Retrievers' Feedback on over 410K query rewrites across 12K conversations. The resulting model achieves state-of-the-art performance on two recent conversational search benchmarks.
• Score: 25.16282868262589
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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 using this dataset to align it with the retrievers' preferences as feedback. The resulting model achieves state-of-the-art performance on two recent conversational search benchmarks, significantly outperforming existing baselines, including GPT-3.5.

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