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 $\sim$$148k$ papers citing LLMs to quantify their influence and reveal trends in their usage patterns. Our analysis reveals not only the increasing prevalence of LLMs in non-CS fields but also the disparities in their usage, with some fields utilizing them more frequently than others since 2018, notably Linguistics and Engineering together accounting for $\sim$$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.

本研究针对大型语言模型（LLMs）在非计算机科学领域的影响进行了系统性分析，填补了相关研究的空白。通过对$106$个LLMs和约$148,000$篇引用LLMs的论文进行定量分析，发现自2018年以来，LLMs在语言学和工程等领域的使用频率显著上升，并揭示了这些领域大多采用无需进一步微调的任务无关型LLMs来解决特定问题。本研究揭示了LLMs在跨学科研究中的重要性和潜在影响。

学术领域的转变：大型语言模型对计算机科学以外学术领域的影响