No Size Fits All: The Perils and Pitfalls of Leveraging LLMs Vary with Company Size

TL;DR

This paper explores how the challenges and strategies for deploying large language models vary across organizations of different sizes, providing insights, case studies, and practical guidance for effective utilization.

Contribution

It offers a novel analysis of LLM deployment issues tailored to organization size, including a case study, review of industrial practices, and a practical implementation guide.

Findings

Challenges differ significantly with company size

Case study insights highlight practical deployment issues

Guidelines improve LLM utilization efficiency

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\footnote{\url{https://github.com/vinayakcse/IndustrialLLMsPapers}} repository with the most recent papers in the field.