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

ArXi:2602.00004v2 Announce Type: replace-cross 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.