Citations in scholarly work serve the essential purpose of acknowledging and
crediting the original sources of knowledge that have been incorporated or
referenced. Depending on their surrounding textual context, these citations are
used for different motivations and purposes. Large Language Models (LLMs) could
be helpful in capturing these fine-grained citation information via the
corresponding textual context, thereby enabling a better understanding towards
the literature. Furthermore, these citations also establish connections among
scientific papers, providing high-quality inter-document relationships and
human-constructed knowledge. Such information could be incorporated into LLMs
pre-training and improve the text representation in LLMs. Therefore, in this
paper, we offer a preliminary review of the mutually beneficial relationship
between LLMs and citation analysis. Specifically, we review the application of
LLMs for in-text citation analysis tasks, including citation classification,
citation-based summarization, and citation recommendation. We then summarize
the research pertinent to leveraging citation linkage knowledge to improve text
representations of LLMs via citation prediction, network structure information,
and inter-document relationship. We finally provide an overview of these
contemporary methods and put forth potential promising avenues in combining
LLMs and citation analysis for further investigation.

本文综述了大语言模型（LLMs）与引文分析之间的相互关系，包括引文分类、基于引文的摘要和引文推荐等任务，以及通过引文预测、网络结构信息和文献间关系等手段来改进 LLMs 的文本表示，并提出了进一步研究 LLMs 和引文分析相结合的潜在方向。