《文獻審查：你引用了，但讀過了嗎？大語言模型時代的科學參考文獻驗證基準》

摘要

科學研究依賴準確的文獻引用以確保歸屬與誠信，然而大型語言模型（LLMs）引入了新風險：虛構的參考文獻看似合理，卻對應不到真實出版物。這類虛幻引用已在多個重要機器學習會議的投稿和錄用論文中被發現，暴露出同行評審的脆弱性。與此同時，快速增長的參考文獻清單使人工核查變得不切實際，現有自動化工具對雜亂異構的引用格式仍顯脆弱，且缺乏標準化評估。我們提出首個針對科學寫作中虛幻引用的綜合性基準測試與檢測框架。通過多智能體驗證流程，我們將引用檢查分解為主張提取、證據檢索、段落匹配、推理校準與判斷，以評估引用來源是否真實支持其論斷。我們構建了跨領域的大規模人工驗證數據集，並定義了引用忠實度與證據一致性的統一指標。針對前沿大型語言模型的實驗揭示出大量引用錯誤，同時表明我們的框架在準確性與可解釋性上均顯著優於現有方法。本研究為LLM時代的引用審計提供了首個可擴展基礎架構，並為提升科學參考文獻的可信度提供了實用工具。

English

Scientific research relies on accurate citation for attribution and integrity, yet large language models (LLMs) introduce a new risk: fabricated references that appear plausible but correspond to no real publications. Such hallucinated citations have already been observed in submissions and accepted papers at major machine learning venues, exposing vulnerabilities in peer review. Meanwhile, rapidly growing reference lists make manual verification impractical, and existing automated tools remain fragile to noisy and heterogeneous citation formats and lack standardized evaluation. We present the first comprehensive benchmark and detection framework for hallucinated citations in scientific writing. Our multi-agent verification pipeline decomposes citation checking into claim extraction, evidence retrieval, passage matching, reasoning, and calibrated judgment to assess whether a cited source truly supports its claim. We construct a large-scale human-validated dataset across domains and define unified metrics for citation faithfulness and evidence alignment. Experiments with state-of-the-art LLMs reveal substantial citation errors and show that our framework significantly outperforms prior methods in both accuracy and interpretability. This work provides the first scalable infrastructure for auditing citations in the LLM era and practical tools to improve the trustworthiness of scientific references.

《文獻審查：你引用了，但讀過了嗎？大語言模型時代的科學參考文獻驗證基準》

CiteAudit: You Cited It, But Did You Read It? A Benchmark for Verifying Scientific References in the LLM Era

摘要

Support