用大模型提升搜索后排序，让结果更符合用户需求。
LLM4PR: Improving Post-Ranking in Search Engine with Large Language Models
- 通过查询引导适配器融合异构特征，生成用户/物品向量。
- 引入语义对齐和双任务学习，显著提升后排序效果。
- 适合关注搜索体验优化的研究者与工程团队。
随着大语言模型（LLMs）的快速发展，其在信息检索（IR）与搜索引擎（SE）中的应用日益广泛。近期研究提出在搜索引擎中增加后排序阶段以提升用户体验，但利用大模型改进后排序的研究仍较少。本文提出一种新范式 LLM4PR，利用大模型能力完成搜索引擎中的后排序任务。具体而言，设计了查询引导适配器（QIA）模块，结合异构特征生成用户/物品表示向量；引入特征适配步骤，使用户/物品表示语义与大模型对齐；最后通过主任务与辅助任务联合训练，实现后排序模型的微调。实验表明，该框架显著优于现有方法，达到当前最优性能。
原文摘要 · Abstract (English)
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.
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