分析14.8万篇论文，揭示大模型在非计算机领域的影响与使用模式。
Transforming Scholarly Landscapes: Influence of Large Language Models on Academic Fields beyond Computer Science
- 基于106个大模型和14.8万篇引用论文，系统分析跨领域影响。
- 2018年后语言学与工程领域占45%的引用，使用频率最高。
- 多数领域直接用零样本/少样本模型解决专业问题，无需微调。
大型语言模型（LLMs）开启了自然语言处理（NLP）的变革时代，重塑了研究范式并拓展至其他学科。然而，现有研究极少评估LLM对非NLP领域的影响力。本文首次系统性地实证分析了LLM在计算机科学以外领域的影响与应用。我们收集了106个主流LLM，并分析了约14.8万篇引用这些模型的论文，以量化其影响力并揭示使用趋势。结果表明，自2018年以来，非计算机领域对LLM的采用呈上升趋势，其中语言学与工程领域合计贡献了约45%的引用。此外，大多数领域主要采用任务无关型的LLM，这些模型具备零样本或少样本学习能力，无需额外微调即可解决特定领域问题。本研究揭示了NLP通过大模型实现的跨学科影响，为理解其机遇与挑战提供了新视角。
原文摘要 · Abstract (English)
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.
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