# LLMs4All: A Review of Large Language Models Across Academic Disciplines

arXiv: https://arxiv.org/abs/2509.19580 · HTML: https://mlsanity.com/paper/2509.19580/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2025.

## Built on by (most cited first, 14 of 14)

- [CiteAudit: You Cited It, But Did You Read It? A Benchmark for Verifying Scientific References in the LLM Era](https://mlsanity.com/paper/2602.23452/) (2026)
- [Drift-Bench: Diagnosing Cooperative Breakdowns in LLM Agents under Input Faults via Multi-Turn Interaction](https://mlsanity.com/paper/2602.02455/) (2026)
- [Why Semantic Entropy Fails: Geometry-Aware and Calibrated Uncertainty for Policy Optimization](https://mlsanity.com/paper/2605.21801/) (2026)
- [Optimizing What Policies Learn From: Recoverability-aware Rollout Intervention Learning](https://mlsanity.com/paper/2608.05080/) (2026)
- [How Open Must Language Models be to Enable Reliable Scientific Inference?](https://mlsanity.com/paper/2603.26539/) (2026)
- [Project Imaging-X: A Survey of 1000+ Open-Access Medical Imaging Datasets for Foundation Model Development](https://mlsanity.com/paper/2603.27460/) (2026)
- [MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU](https://mlsanity.com/paper/2604.05091/) (2026)
- [Agent+P: Guiding UI Agents via Symbolic Planning](https://mlsanity.com/paper/2510.06042/) (2025)
- [BLURR: A Boosted Low-Resource Inference for Vision-Language-Action Models](https://mlsanity.com/paper/2512.11769/) (2025)
- [OPBench: A Graph Benchmark to Combat the Opioid Crisis](https://mlsanity.com/paper/2602.14602/) (2026)
- [Confidence Laundering in Agent Systems: Why Uncertainty Needs a Latent Carrier](https://mlsanity.com/paper/2606.20662/) (2026)
- [Can LLMs Act as Historians? Evaluating Historical Research Capabilities of LLMs via the Chinese Imperial Examination](https://mlsanity.com/paper/2604.24690/) (2026)
- [Nsanku: Evaluating Zero-Shot Translation Performance of LLMs for Ghanaian Languages](https://mlsanity.com/paper/2605.04208/) (2026)
- [CLEAT: Learning Value-Guided Clarification for Interactive Agents](https://mlsanity.com/paper/2610.12655/) (2026)

## Abstract (arXiv)

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.