Large Language Models for Citation Context and Cited Content Recognition: From Boundary Detection to Evidence Grounding

Abstract

Citation analysis has traditionally been organized around citation intent, function, stance, citation context analysis, and cited text span identification. Recent large language models (LLMs) have been applied to these tasks through prompting, in-context learning, fine-tuning, annotation assistance, and retrieval-augmented generation. Yet it remains unclear whether these studies also develop a more explicit account of citation recognition. This survey reviews LLM-based citation analysis through a recognition-centered lens. We define citation recognition as the identification of the textual and semantic units needed for citation understanding, especially citation context, citation content, and cited content. We summarize pre-LLM assumptions about fixed context windows, interpretative labels, and cited text span grounding, and then synthesize five LLM-based method paradigms: prompting and fine-tuning, LLM-assisted annotation, long-context recognition, retrieval-augmented grounding, and multi-dimensional frameworks. We argue that the next stage of LLM-based citation recognition should give closer attention to boundary quality, content consistency, evidence faithfulness, and reproducibility.