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.

翻译：随着大型语言模型（LLM）的出现，生成式检索（GR）革新了文档检索领域，基于LLM的GR正逐渐被工业界采用。尽管其优势显著且潜力巨大，但基于LLM的GR存在幻觉问题，在某些情况下会生成与查询无关的文档，这严重挑战了其在实际应用中的可信度。为此，我们提出了一种优化的GR框架，旨在缓解检索幻觉。该框架在模型训练中集成了知识蒸馏推理，并引入决策代理以进一步提升检索精度。具体而言，我们利用LLM对GR检索到的查询-文档（q-d）对进行评估和推理，然后将推理数据作为迁移知识蒸馏至GR模型。此外，我们采用决策代理作为后处理步骤，通过检索模型扩展GR检索到的文档，并从多角度筛选最相关的文档作为最终的生成式检索结果。在真实数据集上的大量离线实验，以及在支付宝基金搜索和保险搜索上的在线A/B测试，均证明了我们框架在提升搜索质量和转化收益方面的优越性与有效性。