让AI生成的引用更准确，自动链接到对应文献。
C$^2$-Cite: Contextual-Aware Citation Generation for Attributed Large Language Models
- 引入上下文感知机制，让引用符号与内容精准匹配。
- 在三个数据集上，引用质量提升5.8%，回答正确率提升17.4%。
- 适合需要高可信度输出的科研、教育类AI应用。
溯源技术通过为LLM生成的内容添加引用，提升其可信度，使用户可追溯原始来源并验证输出可靠性。然而，现有指令微调的带引文LLM在生成过程中常无法正确理解引用符号（如[i]）的上下文语义，根源在于对引用标记周围上下文信息感知不足，导致引用断裂、检索知识融合不佳。为此，我们提出新型上下文感知引用生成框架C²-Cite，显式建模引用标记与其参考内容间的语义关联。具体而言，采用上下文引用对齐机制：先将检索文档上下文编码至引用符号表示中，再通过引用路由器函数解码信息对齐标记编号。该机制使引用符号从泛化占位符转变为指向源信息的主动知识指针。在ALCE基准测试的三个数据集上，C²-Cite++框架平均提升引用质量5.8%，响应正确率提升17.4%。代码已公开于https://github.com/BAI-LAB/c2cite。
原文摘要 · Abstract (English)
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 citations, then aligns the marker numbers by decoding information from a citation router function. This mechanism enables the transformation of citation markers from generic placeholders into active knowledge pointers that link to the referenced source information. Experimental results on the ALCE benchmark across three datasets validate our framework C$^2$-Cite++: it outperforms the SOTA baseline by an average of 5.8\% in citation quality and 17.4\% in response correctness. The implementation is publicly available at https://github.com/BAI-LAB/c2cite
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