Citation Text Generation (CTG) is a task in natural language processing (NLP)
that aims to produce text that accurately cites or references a cited document
within a source document. In CTG, the generated text draws upon contextual cues
from both the source document and the cited paper, ensuring accurate and
relevant citation information is provided. Previous work in the field of
citation generation is mainly based on the text summarization of documents.
Following this, this paper presents a framework, and a comparative study to
demonstrate the use of Large Language Models (LLMs) for the task of citation
generation. Also, we have shown the improvement in the results of citation
generation by incorporating the knowledge graph relations of the papers in the
prompt for the LLM to better learn the relationship between the papers. To
assess how well our model is performing, we have used a subset of standard
S2ORC dataset, which only consists of computer science academic research papers
in the English Language. Vicuna performs best for this task with 14.15 Meteor,
12.88 Rouge-1, 1.52 Rouge-2, and 10.94 Rouge-L. Also, Alpaca performs best, and
improves the performance by 36.98% in Rouge-1, and 33.14% in Meteor by
including knowledge graphs.

本篇论文介绍了一个基于大型语言模型的引文生成任务的框架，并通过比较研究展示了将知识图谱关系纳入模型输入以提高其表现的结果。