LLMs4All: A Review of Large Language Models Across Academic Disciplines

TL;DR

This comprehensive review surveys state‑of‑the‑art Large Language Models (LLMs) and their cross‑disciplinary applications across arts, letters, law, economics, business, science, and engineering. It outlines foundational concepts, catalogs major model families (GPT series, Claude, Gemini, Gork, Llama, Qwen, DeepSeek, Open‑OSS), and details evaluation frameworks, benchmarks, and performance patterns, while emphasizing governance and safety. The core contribution is a disciplined taxonomy linking disciplinary tasks to LLM capabilities, plus a synthesis of cross‑domain opportunities, limitations, and design guidelines for responsible adoption. The work highlights practical implications for model selection, benchmarking, evaluation rigor, and deployment patterns that balance utility with safety, compliance, and human oversight. Overall, the paper offers a structured blueprint for leveraging LLMs to accelerate research, improve decision‑making, and enable auditable, domain‑grounded AI across diverse fields.

Abstract

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.

Figures (23)

• Figure 1: Key milestones in the development of LLMs.
• Figure 2: The model architecture of Transformer (Figure source: the original paper vaswani2017attention).
• Figure 3: Chronological display of current SOTA LLMs.
• Figure 4: The history research in the era of LLMs.
• Figure 5: The philosophy research in the era of LLMs.