The embedding-based retrieval (EBR) approach is widely used in mainstream search engine retrieval systems and is crucial in recent retrieval-augmented methods for eliminating LLM illusions. However, existing EBR models often face the "semantic drift" problem and insufficient focus on key information, leading to a low adoption rate of retrieval results in subsequent steps. This issue is especially noticeable in real-time search scenarios, where the various expressions of popular events on the Internet make real-time retrieval heavily reliant on crucial event information. To tackle this problem, this paper proposes a novel approach called EER, which enhances real-time retrieval performance by improving the dual-encoder model of traditional EBR. We incorporate contrastive learning to accompany pairwise learning for encoder optimization. Furthermore, to strengthen the focus on critical event information in events, we include a decoder module after the document encoder, introduce a generative event triplet extraction scheme based on prompt-tuning, and correlate the events with query encoder optimization through comparative learning. This decoder module can be removed during inference. Extensive experiments demonstrate that EER can significantly improve the real-time search retrieval performance. We believe that this approach will provide new perspectives in the field of information retrieval. The codes and dataset are available at https://github.com/open-event-hub/Event-enhanced_Retrieval .

翻译：基于嵌入的检索（EBR）方法广泛应用于主流搜索引擎检索系统，且在近期检索增强方法中对于消除大型语言模型（LLM）幻觉至关重要。然而，现有EBR模型常面临"语义漂移"问题及对关键信息关注不足，导致检索结果在后续步骤中的采纳率较低。这一现象在实时检索场景中尤为显著——互联网上热门事件的多样化表达使得实时检索严重依赖关键事件信息。针对该问题，本文提出一种名为EER的新方法，通过改进传统EBR的双编码器模型来增强实时检索性能。我们引入对比学习配合成对学习进行编码器优化。此外，为强化对事件中关键事件信息的关注，我们在文档编码器后添加解码器模块，提出基于提示调优的生成式事件三元组提取方案，并通过对比学习将事件与查询编码器优化相关联。该解码器模块在推理阶段可移除。大量实验表明，EER能显著提升实时搜索检索性能。我们相信该方法将为信息检索领域提供新视角。相关代码及数据集可从 https://github.com/open-event-hub/Event-enhanced_Retrieval 获取。