[2602.00004] C$^2$-Cite: Contextual-Aware Citation Generation for Attributed Large Language Models

Computer Science > Information Retrieval arXiv:2602.00004 (cs) [Submitted on 19 Nov 2025 (v1), last revised 23 Mar 2026 (this version, v2)] Title:C$^2$-Cite: Contextual-Aware Citation Generation for Attributed Large Language Models Authors:Yue Yu, Ting Bai, HengZhi Lan, Li Qian, Li Peng, Jie Wu, Wei Liu, Jian Luan, Chuan Shi View a PDF of the paper titled C$^2$-Cite: Contextual-Aware Citation Generation for Attributed Large Language Models, by Yue Yu and 8 other authors View PDF Abstract:The attribution technique enhances the credibility of LLMs by adding citations to the generated sentences, enabling users to trace back to the original sources and verify the reliability of the output. However, existing instruction-tuned attributed LLMs often fail to properly interpret the contextual semantics of citation symbols (e.g., [i]) during text generation. This shortcoming arises from their insufficient awareness of the context information surrounding citation markers, which in turn leads to disjointed references and poor integration of retrieved knowledge into the generated content. To address this issue, we propose a novel \textbf{C}ontextual-aware \textbf{C}itation generation framework (\textbf{C$^2$}-\textbf{Cite}) that explicitly integrates the semantic relationships between citation markers and their referenced content. Specifically, a contextual citation alignment mechanism is adopted: it first encodes the retrieved document contexts into the symbol representation of citati...