让AI在本地自动找可信学术引用，防止幻觉和泄露
CiteLLM: An Agentic Platform for Trustworthy Scientific Reference Discovery
- 将AI嵌入LaTeX编辑器，全程本地运行不传数据
- 动态路由从可信学术库检索，确保引用真实有效
- 适合写论文时需要快速验证引用的科研人员
大语言模型（LLMs）为提升学术效率带来新机遇，但其伦理应用仍面临三大挑战：（1）生成内容的可信度，（2）学术诚信与知识产权保护，（3）信息隐私安全。本文提出CiteLLM，一个专为可信文献发现设计的智能体平台，用于支撑作者陈述的文献依据。系统将LLM功能直接集成至LaTeX编辑环境，实现无缝体验且无数据外传。为确保无幻觉引用，采用动态学科感知路由，仅从可信网络学术资源库中检索候选文献；同时利用LLM生成上下文相关的搜索查询、按相关性排序，并通过段落级语义匹配与集成聊天机器人验证和解释支持关系。评估结果表明，该系统在返回有效且高度可用参考文献方面表现优异。
原文摘要 · Abstract (English)
Large language models (LLMs) have created new opportunities to enhance the efficiency of scholarly activities; however, challenges persist in the ethical deployment of AI assistance, including (1) the trustworthiness of AI-generated content, (2) preservation of academic integrity and intellectual property, and (3) protection of information privacy. In this work, we present CiteLLM, a specialized agentic platform designed to enable trustworthy reference discovery for grounding author-drafted claims and statements. The system introduces a novel interaction paradigm by embedding LLM utilities directly within the LaTeX editor environment, ensuring a seamless user experience and no data transmission outside the local system. To guarantee hallucination-free references, we employ dynamic discipline-aware routing to retrieve candidates exclusively from trusted web-based academic repositories, while leveraging LLMs solely for generating context-aware search queries, ranking candidates by relevance, and validating and explaining support through paragraph-level semantic matching and an integrated chatbot. Evaluation results demonstrate the superior performance of the proposed system in returning valid and highly usable references.
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