Overall semantic scholar dataset statistics.

Large Language Models (LLMs) have ushered in a transformative era in Natural Language Processing (NLP), reshaping research and extending NLP’s influence to other fields of study. However, there is little to no work examining the degree to which LLMs influence other research fields. This work empirically and systematically examines the influence and use of LLMs in fields beyond NLP. We curate 106 LLMs and analyze ∼148k papers citing LLMs to quantify their influence and reveal trends in their usage patterns. Our analysis reveals not only the increasing prevalence of LLMs in non-CS fields but also the disparities in their usage, with some fields utilizing them more frequently than others since 2018, notably Linguistics and Engineering together accounting for ∼45% of LLM citations. Our findings further indicate that most of these fields predominantly employ task-agnostic LLMs, proficient in zero or few-shot learning without requiring further fine-tuning, to address their domain-specific problems. This study sheds light on the cross-disciplinary impact of NLP through LLMs, providing a better understanding of the opportunities and challenges.大语言模型（Large Language Models，LLMs）开启了自然语言处理（Natural Language Processing，NLP）的变革性时代，重塑了该领域的研究格局，并将NLP的影响力拓展至其他学科。然而目前鲜有乃至几乎没有研究探讨大语言模型对其他研究领域的影响程度。本研究以实证且系统的方式，考察了大语言模型在NLP以外领域的应用与影响力。我们整理了106个大语言模型，并对约14.8万篇引用大语言模型的论文展开分析，以量化其影响力并揭示其使用模式的趋势。分析结果显示，自2018年以来，大语言模型在非计算机科学领域的普及率持续攀升，同时其使用也存在不均衡性：部分领域的应用频率显著高于其他领域，其中语言学与工程学领域的引用量合计约占大语言模型总引用量的45%。进一步研究发现，绝大多数领域主要采用无需额外微调、擅长零样本或少样本学习的与任务无关的大语言模型，以解决其领域内的特定问题。本研究阐明了大语言模型所带来的NLP跨学科影响力，有助于更深入地理解其中蕴含的机遇与挑战。