@inproceedings{urlana-etal-2025-size,
title = "No Size Fits All: The Perils and Pitfalls of Leveraging {LLM}s Vary with Company Size",
author = "Urlana, Ashok and
Vinayak Kumar, Charaka and
Garlapati, Bala Mallikarjunarao and
Singh, Ajeet Kumar and
Mishra, Rahul",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven and
Darwish, Kareem and
Agarwal, Apoorv",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics: Industry Track",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-industry.16/",
pages = "187--203",
abstract = "Large language models (LLMs) are playing a pivotal role in deploying strategic use cases across a range of organizations, from large pan-continental companies to emerging startups. The issues and challenges involved in the successful utilization of LLMs can vary significantly depending on the size of the organization. It is important to study and discuss these pertinent issues of LLM adaptation with a focus on the scale of the industrial concerns and brainstorm possible solutions and prospective directions. Such a study has not been prominently featured in the current research literature. In this study, we adopt a threefold strategy: first, we conduct a case study with industry practitioners to formulate the key research questions; second, we examine existing industrial publications to address these questions; and finally, we provide a practical guide for industries to utilize LLMs more efficiently. We release the GitHub repository with the most recent papers in the field."
}
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%0 Conference Proceedings
%T No Size Fits All: The Perils and Pitfalls of Leveraging LLMs Vary with Company Size
%A Urlana, Ashok
%A Vinayak Kumar, Charaka
%A Garlapati, Bala Mallikarjunarao
%A Singh, Ajeet Kumar
%A Mishra, Rahul
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%Y Darwish, Kareem
%Y Agarwal, Apoorv
%S Proceedings of the 31st International Conference on Computational Linguistics: Industry Track
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F urlana-etal-2025-size
%X Large language models (LLMs) are playing a pivotal role in deploying strategic use cases across a range of organizations, from large pan-continental companies to emerging startups. The issues and challenges involved in the successful utilization of LLMs can vary significantly depending on the size of the organization. It is important to study and discuss these pertinent issues of LLM adaptation with a focus on the scale of the industrial concerns and brainstorm possible solutions and prospective directions. Such a study has not been prominently featured in the current research literature. In this study, we adopt a threefold strategy: first, we conduct a case study with industry practitioners to formulate the key research questions; second, we examine existing industrial publications to address these questions; and finally, we provide a practical guide for industries to utilize LLMs more efficiently. We release the GitHub repository with the most recent papers in the field.
%U https://aclanthology.org/2025.coling-industry.16/
%P 187-203
Markdown (Informal)
[No Size Fits All: The Perils and Pitfalls of Leveraging LLMs Vary with Company Size](https://aclanthology.org/2025.coling-industry.16/) (Urlana et al., COLING 2025)
ACL