LLMs4All: A Review on Large Language Models for Research and
  Applications in Academic Disciplines
Authors: name: Yanfang, name: Ye, name: Zheyuan Zhang, name: Tianyi Ma, name: Zehong Wang, name: Yiyang Li, name: Shifu Hou, name: Weixiang Sun, name: Kaiwen Shi, name: Yijun Ma, name: Wei Song, name: Ahmed Abbasi, name: Ying Cheng, name: Jane Cleland-Huang, name: Steven Corcelli, name: Patricia Culligan, name: Robert Goulding, name: Ming Hu, name: Ting Hua, name: John Lalor, name: Fang Liu, name: Tengfei Luo, name: Ed Maginn, name: Nuno Moniz, name: Jason Rohr, name: Brett Savoie, name: Daniel Slate, name: Tom Stapleford, name: Matthew Webber, name: Olaf Wiest, name: Johnny Zhang, name: Nitesh Chawla

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.