LLMs4All：大型语言模型在学术研究与应用中的综述
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
尖端的人工智慧（AI）技術持續重塑我們對世界的看法。例如，基於大型語言模型（LLMs）的應用程式，如ChatGPT，已展現出在廣泛主題上生成類人對話的能力。由於其在多種語言相關任務（如開放領域問答、翻譯和文件摘要）上的卓越表現，我們可以預見LLMs在更廣泛的現實世界應用（如客戶服務、教育與無障礙、科學發現）中可能帶來的深遠影響。受其成功啟發，本文將概述最先進的LLMs及其在廣泛學術領域中的整合，包括：（1）藝術、文學與法律（如歷史、哲學、政治學、藝術與建築、法律），（2）經濟與商業（如金融、經濟學、會計、市場行銷），以及（3）科學與工程（如數學、物理與機械工程、化學與化學工程、生命科學與生物工程、地球科學與土木工程、電腦科學與電機工程）。結合人文與科技，本文將探討LLMs如何塑造這些領域的研究與實踐，同時也討論生成式AI時代的關鍵限制、開放挑戰與未來方向。對LLMs跨學科應用的回顧，以及關鍵觀察與見解，將有助於對利用LLMs推動多樣化現實世界應用感興趣的研究者與實踐者。
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.