REALM: A Dataset of Real-World LLM Use Cases
Jingwen Cheng, Kshitish Ghate, Wenyue Hua, William Yang Wang, Hong Shen, Fei Fang
Abstract
Large Language Models (LLMs), such as the GPT series, have driven significant industrial applications, leading to economic and societal transformations. However, a comprehensive understanding of their real-world applications remains limited.To address this, we introduce **REALM**, a dataset of over 94,000 LLM use cases collected from Reddit and news articles. **REALM** captures two key dimensions: the diverse applications of LLMs and the demographics of their users. It categorizes LLM applications and explores how users’ occupations relate to the types of applications they use.By integrating real-world data, **REALM** offers insights into LLM adoption across different domains, providing a foundation for future research on their evolving societal roles. An interactive dashboard ([https://realm-e7682.web.app/](https://realm-e7682.web.app/)) is provided for easy exploration of the dataset.
BibTeX
@inproceedings{cheng-etal-2025-realm,
title = "{REALM}: A Dataset of Real-World {LLM} Use Cases",
author = "Cheng, Jingwen and
Ghate, Kshitish and
Hua, Wenyue and
Wang, William Yang and
Shen, Hong and
Fang, Fei",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.437/",
doi = "10.18653/v1/2025.findings-acl.437",
pages = "8331--8341",
ISBN = "979-8-89176-256-5"
}