@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",
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."
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%0 Conference Proceedings
%T REALM: A Dataset of Real-World LLM Use Cases
%A Cheng, Jingwen
%A Ghate, Kshitish
%A Hua, Wenyue
%A Wang, William Yang
%A Shen, Hong
%A Fang, Fei
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F cheng-etal-2025-realm
%X 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.
%R 10.18653/v1/2025.findings-acl.437
%U https://aclanthology.org/2025.findings-acl.437/
%U https://doi.org/10.18653/v1/2025.findings-acl.437
%P 8331-8341
Markdown (Informal)
[REALM: A Dataset of Real-World LLM Use Cases](https://aclanthology.org/2025.findings-acl.437/) (Cheng et al., Findings 2025)
ACL
- Jingwen Cheng, Kshitish Ghate, Wenyue Hua, William Yang Wang, Hong Shen, and Fei Fang. 2025. REALM: A Dataset of Real-World LLM Use Cases. In Findings of the Association for Computational Linguistics: ACL 2025, pages 8331–8341, Vienna, Austria. Association for Computational Linguistics.