“大型语言模型(LLM) 在各种自然语言任务上表现出了卓越的性能,但它们很容易受到过时数据和特定领域限制的影响。为了应对这些挑战,研究人员整合不同来源的外部信息来增强大语言模型,具体方法如检索增强等。在本文中,我们综合讨论了检索增强技术的发展趋势,包括检索时机规划、检索技术、以及检索结果的利用。此外,我们介绍了当前可用于检索增强任务的数据集和评价方法,并指出了应用和潜在研究方向。我们希望这项综述能够为社区提供对该研究领域的快速了解和全面概述,以启发未来的研究工作。”

Abstractive dialogue summarization is the task of capturing the highlights of a dialogue andrewriting them into a concise version. In this paper we present a novel multi-speaker dialogue summarizer to demonstrate how large-scale commonsense knowledge can facilitate dialogue un-derstanding and summary generation. In detail we consider utterance and commonsense knowl-edge as two different types of data and design a Dialogue Heterogeneous Graph Network (D-HGN) for modeling both information. Meanwhile we also add speakers as heterogeneous nodes to facilitate information flow. Experimental results on the SAMSum dataset show that our modelcan outperform various methods. We also conduct zero-shot setting experiments on the Argu-mentative Dialogue Summary Corpus the results show that our model can better generalized tothe new domain.