改变学术景观:大型语言模型对计算机科学以外的学术领域的影响
Aniket Pramanick1, Yufang Hou2, Saif M Mohammad3
1Department of Computer Science, Technical University of Darmstadt, Darmstadt, Germany.
PloS one
|January 14, 2026
概括
大型语言模型 (LLM) 越来越多地被用于计算机科学以外的各种研究领域. 这项研究量化了它们的影响,并揭示了如何采用这些强大的NLP工具的趋势.
科学领域:
- 计算机科学 计算机科学
- 自然语言处理自然语言处理.
- 跨学科研究跨学科研究
背景情况:
- 大型语言模型 (LLM) 已经彻底改变了自然语言处理 (NLP).
- 它们在计算机科学以外的领域的影响力和采用程度仍未得到充分研究.
- 了解这种跨学科影响对于未来的研究方向至关重要.
研究的目的:
- 实证和系统地分析LLMs在NLP以外的研究领域的影响和使用模式.
- 量化LLM在非计算机科学领域的采用程度.
- 识别不同学科LLM利用的趋势和差异.
主要方法:
- 策划了106个大型语言模型的数据集.
- 分析了大约148,000篇引用LLMs的研究论文.
- 自2018年以来,量化了LLM的影响力和确定了使用趋势.
主要成果:
- 在非计算机科学领域,LLM的普及率正在增加.
- 跨学科的LLM采用率存在显著差异.
- 语言学和工程学显示了高的LLM引用率,占2018年以来的~45%.
- 大多数领域主要使用任务无关的LLM,利用零或少数射击的学习能力.
结论:
- 在NLP之外,LLM正在显著影响各种研究领域.
- 任务无关的LLM被广泛采用用于特定领域的解决问题.
- 本研究提供了关于LLM整合在跨学科研究中的机会和挑战的见解.
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