arXiv:2509.19580cs.CL2025-09综述被引 15

综述大模型在文理工商各学科的应用现状与挑战

LLMs4All: A Review of Large Language Models Across Academic Disciplines

  • 系统梳理大模型在人文、经管、理工等领域的应用方法
  • 涵盖历史、法律、金融、生物工程等10余类学科实践
  • 适合关注AI跨学科应用的研究者与行业从业者

前沿人工智能技术持续重塑世界认知。基于大语言模型(LLMs)的应用如ChatGPT已展现出生成人类级对话的能力。由于在开放域问答、翻译、文档摘要等任务中表现优异,大模型有望在客服、教育、科研等领域带来深远影响。本文综述了当前最先进的大模型及其在多个学术领域的整合情况,包括:(1) 文艺、法律领域(如历史、哲学、政治学、艺术与建筑、法学),(2) 经济与商业领域(如金融、经济学、会计、市场营销),(3) 科学与工程领域(如数学、物理与机械工程、化学与化工、生命科学与生物工程、地球科学与土木工程、计算机与电子工程)。本文探讨大模型如何塑造这些领域的研究与实践,同时分析其关键局限、开放挑战及生成式AI时代的未来方向。该跨学科整合的观察与洞见,可帮助研究人员和实践者更有效地利用大模型推动多样化现实应用。

原文摘要 · Abstract (English)

Cutting-edge Artificial Intelligence (AI) techniques keep reshaping our view of the world. For example, Large Language Models (LLMs) based applications such as ChatGPT have shown the capability of generating human-like conversation on extensive topics. Due to the impressive performance on a variety of language-related tasks (e.g., open-domain question answering, translation, and document summarization), one can envision the far-reaching impacts that can be brought by the LLMs with broader real-world applications (e.g., customer service, education and accessibility, and scientific discovery). Inspired by their success, this paper will offer an overview of state-of-the-art LLMs and their integration into a wide range of academic disciplines, including: (1) arts, letters, and law (e.g., history, philosophy, political science, arts and architecture, law), (2) economics and business (e.g., finance, economics, accounting, marketing), and (3) science and engineering (e.g., mathematics, physics and mechanical engineering, chemistry and chemical engineering, life sciences and bioengineering, earth sciences and civil engineering, computer science and electrical engineering). Integrating humanity and technology, in this paper, we will explore how LLMs are shaping research and practice in these fields, while also discussing key limitations, open challenges, and future directions in the era of generative AI. The review of how LLMs are engaged across disciplines-along with key observations and insights-can help researchers and practitioners interested in exploiting LLMs to advance their works in diverse real-world applications.

大模型跨学科综述AI应用

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