LLMs4All：面向学术研究与应用的大型语言模型综述
LLMs4All: A Review on Large Language Models for Research and Applications in Academic Disciplines
September 23, 2025
作者: Yanfang, Ye, Zheyuan Zhang, Tianyi Ma, Zehong Wang, Yiyang Li, Shifu Hou, Weixiang Sun, Kaiwen Shi, Yijun Ma, Wei Song, Ahmed Abbasi, Ying Cheng, Jane Cleland-Huang, Steven Corcelli, Patricia Culligan, Robert Goulding, Ming Hu, Ting Hua, John Lalor, Fang Liu, Tengfei Luo, Ed Maginn, Nuno Moniz, Jason Rohr, Brett Savoie, Daniel Slate, Tom Stapleford, Matthew Webber, Olaf Wiest, Johnny Zhang, Nitesh Chawla
cs.AI
摘要
尖端人工智能（AI）技术持续重塑我们对世界的认知。例如，基于大型语言模型（LLMs）的应用，如ChatGPT，已展现出在广泛话题上生成类人对话的能力。鉴于其在多种语言相关任务（如开放域问答、翻译和文档摘要）上的卓越表现，人们可以预见LLMs在更广泛的实际应用领域（如客户服务、教育与无障碍服务、科学发现）将带来的深远影响。受其成功启发，本文将对最先进的LLMs及其融入多学科领域的情况进行概述，包括：（1）人文、文学与法律（如历史、哲学、政治学、艺术与建筑、法律），（2）经济与商业（如金融、经济学、会计、市场营销），以及（3）科学与工程（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、计算机科学与电气工程）。本文融合人文与技术，探讨LLMs如何塑造这些领域的研究与实践，同时讨论生成式AI时代的关键局限、开放挑战及未来方向。通过跨学科视角审视LLMs的应用，结合关键观察与洞见，本文旨在帮助有意利用LLMs推动多样化实际应用的研究者与实践者，促进其工作进展。
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.