用大模型自动检测论文引用错误，提升科研出版可靠性。
Detecting Reference Errors in Scientific Literature with Large Language Models
- 构建专家标注的论文语句-参考文献配对数据集。
- 无需微调即可在有限上下文中准确识别引用错误。
- 适合科研编辑、审稿人及学术写作助手使用。
引用错误（如引文与引述错误）在科学论文中普遍存在，可能导致错误信息传播，但人工检测耗时费力，成为科学出版的一大挑战。本文评估了OpenAI GPT系列大模型在检测引述错误方面的能力。我们从期刊文章中构建了一个专家标注的通用领域语句-参考文献配对数据集，并在不同设置下测试模型性能，包括通过检索增强提供不同程度的参考信息。结果显示，大模型在仅有少量上下文且未经微调的情况下，仍能有效识别引用错误。本研究为利用人工智能辅助科学论文撰写、评审与出版提供了支持，同时也探讨了该任务未来改进的潜在方向。
原文摘要 · Abstract (English)
Reference errors, such as citation and quotation errors, are common in scientific papers. Such errors can result in the propagation of inaccurate information, but are difficult and time-consuming to detect, posing a significant challenge to scientific publishing. To support automatic detection of reference errors, this work evaluated the ability of large language models in OpenAI's GPT family to detect quotation errors. Specifically, we prepared an expert-annotated, general-domain dataset of statement-reference pairs from journal articles. Large language models were evaluated in different settings with varying amounts of reference information provided by retrieval augmentation. Our results showed that large language models are able to detect erroneous citations with limited context and without fine-tuning. This study contributes to the growing literature that seeks to utilize artificial intelligence to assist in the writing, reviewing, and publishing of scientific papers. Potential avenues for further improvements in this task are also discussed.
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