Citation text plays a pivotal role in elucidating the connection between scientific documents, demanding an in-depth comprehension of the cited paper. Constructing citations is often time-consuming, requiring researchers to delve into extensive literature and grapple with articulating relevant content. To address this challenge, the field of citation text generation (CTG) has emerged. However, while earlier methods have primarily centered on creating single-sentence citations, practical scenarios frequently necessitate citing multiple papers within a single paragraph. To bridge this gap, we propose a method that leverages Large Language Models (LLMs) to generate multi-citation sentences. Our approach involves a single source paper and a collection of target papers, culminating in a coherent paragraph containing multi-sentence citation text. Furthermore, we introduce a curated dataset named MCG-S2ORC, composed of English-language academic research papers in Computer Science, showcasing multiple citation instances. In our experiments, we evaluate three LLMs LLaMA, Alpaca, and Vicuna to ascertain the most effective model for this endeavor. Additionally, we exhibit enhanced performance by integrating knowledge graphs from target papers into the prompts for generating citation text. This research underscores the potential of harnessing LLMs for citation generation, opening a compelling avenue for exploring the intricate connections between scientific documents.

翻译：引用文本在阐明科学文献间的联系方面起着关键作用，要求对被引论文有深入理解。构建引用通常耗时，需要研究者深入阅读大量文献并努力阐述相关内容。为应对这一挑战，引用文本生成（CTG）领域应运而生。然而，早期方法主要集中于生成单句引用，实际场景中常需在一个段落内引用多篇论文。为填补这一空白，我们提出一种利用大语言模型（LLMs）生成多引用句子的方法。该方法涉及单一源论文与一组目标论文，最终生成包含多句引用文本的连贯段落。此外，我们引入了一个名为MCG-S2ORC的精选数据集，由计算机科学领域的英文研究论文构成，展示了多重引用实例。实验中，我们评估了三种LLMs（LLaMA、Alpaca和Vicuna），以确定完成该任务的最有效模型。通过将目标论文的知识图谱整合到生成引用文本的提示中，我们进一步展示了性能提升。本研究强调了利用LLMs进行引用生成的潜力，为探索科学文献间的复杂联系开辟了一条引人入胜的路径。