Alongside the rapid development of Large Language Models (LLMs), there has been a notable increase in efforts to integrate LLM techniques in information retrieval (IR) and search engines (SE). Recently, an additional post-ranking stage is suggested in SE to enhance user satisfaction in practical applications. Nevertheless, research dedicated to enhancing the post-ranking stage through LLMs remains largely unexplored. In this study, we introduce a novel paradigm named Large Language Models for Post-Ranking in search engine (LLM4PR), which leverages the capabilities of LLMs to accomplish the post-ranking task in SE. Concretely, a Query-Instructed Adapter (QIA) module is designed to derive the user/item representation vectors by incorporating their heterogeneous features. A feature adaptation step is further introduced to align the semantics of user/item representations with the LLM. Finally, the LLM4PR integrates a learning to post-rank step, leveraging both a main task and an auxiliary task to fine-tune the model to adapt the post-ranking task. Experiment studies demonstrate that the proposed framework leads to significant improvements and exhibits state-of-the-art performance compared with other alternatives.

翻译：随着大语言模型（LLMs）的快速发展，将LLM技术整合到信息检索（IR）和搜索引擎（SE）中的努力显著增加。近期，有研究建议在搜索引擎中增加一个后排序阶段，以提升实际应用中的用户满意度。然而，专门利用LLMs来增强后排序阶段的研究仍基本处于空白。在本研究中，我们提出了一种名为“搜索引擎中大语言模型用于后排序”（LLM4PR）的新范式，该范式利用LLMs的能力来完成搜索引擎中的后排序任务。具体而言，我们设计了一个查询指令适配器（QIA）模块，通过整合用户/项目的异构特征来推导其表示向量。进一步引入了特征适配步骤，以对齐用户/项目表示与LLM的语义。最后，LLM4PR集成了一个学习后排序步骤，利用主任务和辅助任务来微调模型，使其适应后排序任务。实验研究表明，与其他替代方案相比，所提出的框架带来了显著改进，并展现出最先进的性能。