When Large Language Models Meet Citation: A Survey

TL;DR

This survey explores how Large Language Models can enhance citation analysis and how citation information can improve LLMs' understanding of scholarly literature, highlighting recent methods and future directions.

Contribution

It provides a comprehensive review of the intersection between LLMs and citation analysis, including applications and methods for integrating citation knowledge into LLMs.

Findings

LLMs can be used for citation classification, summarization, and recommendation.

Citation linkage knowledge can improve LLMs' text representations.

The survey identifies promising future research directions.

Abstract

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.