Citation practices are crucial in shaping the structure of scientific knowledge, yet they are often influenced by contemporary norms and biases. The emergence of Large Language Models (LLMs) introduces a new dynamic to these practices. Interestingly, the characteristics and potential biases of references recommended by LLMs that entirely rely on their parametric knowledge, and not on search or retrieval-augmented generation, remain unexplored. Here, we analyze these characteristics in an experiment using a dataset from AAAI, NeurIPS, ICML, and ICLR, published after GPT-4's knowledge cut-off date. In our experiment, LLMs are tasked with suggesting scholarly references for the anonymized in-text citations within these papers. Our findings reveal a remarkable similarity between human and LLM citation patterns, but with a more pronounced high citation bias, which persists even after controlling for publication year, title length, number of authors, and venue. The results hold for both GPT-4, and the more capable models GPT-4o and Claude 3.5 where the papers are part of the training data. Additionally, we observe a large consistency between the characteristics of LLM's existing and non-existent generated references, indicating the model's internalization of citation patterns. By analyzing citation graphs, we show that the references recommended are embedded in the relevant citation context, suggesting an even deeper conceptual internalization of the citation networks. While LLMs can aid in citation generation, they may also amplify existing biases, such as the Matthew effect, and introduce new ones, potentially skewing scientific knowledge dissemination.

翻译：引用实践对于塑造科学知识结构至关重要，然而它们常常受到当代规范与偏见的影响。大型语言模型（LLMs）的出现为这些实践引入了新的动态。有趣的是，完全依赖其参数化知识（而非基于搜索或检索增强生成）的LLMs所推荐参考文献的特征及潜在偏见尚未得到探索。本文通过使用发表于GPT-4知识截止日期之后的AAAI、NeurIPS、ICML和ICLR数据集进行实验，分析了这些特征。实验中，LLMs的任务是为这些论文中的匿名文中引用推荐学术参考文献。我们的研究结果揭示了人类与LLM引用模式之间存在显著相似性，但LLMs表现出更明显的高被引偏见，即使在控制了发表年份、标题长度、作者数量和会议场所后，该偏见依然存在。这一结论对GPT-4以及能力更强的GPT-4o和Claude 3.5模型均成立（相关论文已包含在训练数据中）。此外，我们观察到LLM生成的现存参考文献与虚构参考文献在特征上高度一致，表明模型已内化了引用模式。通过分析引用图谱，我们证明推荐的参考文献被嵌入到相关的引用语境中，这暗示着模型对引用网络实现了更深层次的概念内化。尽管LLMs能够辅助生成引用，但它们也可能放大现有偏见（如马太效应）并引入新的偏见，从而可能扭曲科学知识的传播。