AI 中文总结
研究针对大语言模型在学术写作中产生的幻觉式和可疑引用问题，评估了CheckIfExist等工具，指出其虽能预警，但受多种因素限制，强调此类引用对科学交流是个现实且渐趋严重的问题，需更完善的检测系统。
AI 中文摘要
大语言模型在学术写作中使用日益广泛，引发对幻觉式和不可靠引用的担忧。近期研究表明此问题已普遍且在文献和会议中愈发常见。本文综述相关研究并评估检测幻觉式引用的工具，如CheckIfExist等。虽这些工具能提供预警，但受引用提取错误、元数据不完整、数据库覆盖有限及验证结果不一致等限制。我们认为此类引用对科学交流是个且日益严重的问题，仍需更透明和多源的检测系统。
英文摘要
Large language models are increasingly used in academic writing, including for reference generation, raising concerns about hallucinated and unreliable citations. Recent research suggests that this problem is already widespread and is becoming increasingly prevalent in the published literature and at scientific conferences. In this position paper, we review recent studies on hallucinated references and evaluate several currently available tools for detecting problematic references using documents containing hallucinated citations. The tools assessed include CheckIfExist, HalluCiteChecker, Hallucinator, Hallucinated Reference Finder (HalRef), and RefChecker. While these systems can provide useful early warnings in many cases, their performance is limited by reference extraction errors, incomplete metadata, limited database coverage, and inconsistent verification results. We argue that hallucinated and suspicious references have become a real and growing problem for scientific communication, and that more transparent and multi-source detection systems are still needed.
Comments6 pages, 4 tables
