Alleviating LLM-based Generative Retrieval Hallucination in Alipay Search
Yedan Shen, Kaixin Wu, Yuechen Ding, Jingyuan Wen, Hong Liu, Mingjie Zhong, Zhouhan Lin, Jia Xu, Linjian Mo
TL;DR
Generative retrieval using large language models can hallucinate and retrieve irrelevant documents in large-scale search. The authors propose an optimized framework for GR that combines knowledge distillation reasoning with a post-processing decision agent to reduce hallucinations and improve precision, without requiring extra manually annotated data. Key components include constructing reasoning data with a larger LM and distilling it into a smaller GR model via $L_{reasoning}$, and validating GR outputs through a multi-perspective decision agent. Offline evaluations on Alipay Fund and Insurance searches show consistent accuracy gains, while online A/B tests demonstrate improved user engagement and conversions, highlighting practical impact for industrial search systems.
Abstract
Generative retrieval (GR) has revolutionized document retrieval with the advent of large language models (LLMs), and LLM-based GR is gradually being adopted by the industry. Despite its remarkable advantages and potential, LLM-based GR suffers from hallucination and generates documents that are irrelevant to the query in some instances, severely challenging its credibility in practical applications. We thereby propose an optimized GR framework designed to alleviate retrieval hallucination, which integrates knowledge distillation reasoning in model training and incorporate decision agent to further improve retrieval precision. Specifically, we employ LLMs to assess and reason GR retrieved query-document (q-d) pairs, and then distill the reasoning data as transferred knowledge to the GR model. Moreover, we utilize a decision agent as post-processing to extend the GR retrieved documents through retrieval model and select the most relevant ones from multi perspectives as the final generative retrieval result. Extensive offline experiments on real-world datasets and online A/B tests on Fund Search and Insurance Search in Alipay demonstrate our framework's superiority and effectiveness in improving search quality and conversion gains.