LLMs4All: A Review of Large Language Models Across Academic Disciplines
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2025
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| author | Ye, Yanfang Zhang, Zheyuan Ma, Tianyi Wang, Zehong Li, Yiyang Hou, Shifu Sun, Weixiang Shi, Kaiwen Ma, Yijun Song, Wei Abbasi, Ahmed Cheng, Ying Cleland-Huang, Jane Corcelli, Steven Goulding, Robert Hu, Ming Hua, Ting Lalor, John Liu, Fang Luo, Tengfei Maginn, Edward Moniz, Nuno Rohr, Jason Savoie, Brett Slate, Daniel Webber, Matthew Wiest, Olaf Zhang, Johnny Chawla, Nitesh V. |
| author_facet | Ye, Yanfang Zhang, Zheyuan Ma, Tianyi Wang, Zehong Li, Yiyang Hou, Shifu Sun, Weixiang Shi, Kaiwen Ma, Yijun Song, Wei Abbasi, Ahmed Cheng, Ying Cleland-Huang, Jane Corcelli, Steven Goulding, Robert Hu, Ming Hua, Ting Lalor, John Liu, Fang Luo, Tengfei Maginn, Edward Moniz, Nuno Rohr, Jason Savoie, Brett Slate, Daniel Webber, Matthew Wiest, Olaf Zhang, Johnny Chawla, Nitesh V. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_19580 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LLMs4All: A Review of Large Language Models Across Academic Disciplines Ye, Yanfang Zhang, Zheyuan Ma, Tianyi Wang, Zehong Li, Yiyang Hou, Shifu Sun, Weixiang Shi, Kaiwen Ma, Yijun Song, Wei Abbasi, Ahmed Cheng, Ying Cleland-Huang, Jane Corcelli, Steven Goulding, Robert Hu, Ming Hua, Ting Lalor, John Liu, Fang Luo, Tengfei Maginn, Edward Moniz, Nuno Rohr, Jason Savoie, Brett Slate, Daniel Webber, Matthew Wiest, Olaf Zhang, Johnny Chawla, Nitesh V. Computation and Language 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. |
| title | LLMs4All: A Review of Large Language Models Across Academic Disciplines |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2509.19580 |