Analysis of Small Large Language Models(LLMs)
The performance, functionality, and usability of Small Large Language Models are analyzed to understand how they can be effectively used in various natural language processing (NLP) tasks to evaluate what advantages and disadvantages small models have compared to large models, and whether they can be optimized for specific tasks.
Abstract
The trend of Small Large Language Models (LLMs) has been developing significantly in recent years. Lightweight LLMs are designed to operate efficiently on mobile devices or edge computing environments, and perform well even with limited resources. These models are optimized for specific domains and provide results that meet the needs of specific industries. In addition, they are easily accessible to non-developers due to their user-friendly interfaces, and are utilized in various fields. The purpose of this paper is to analyze the performance, functionality, and usability of Small Large Language Models (LLMs) to understand how they can be effectively used in various natural language processing (NLP) tasks. In particular, the key goal is to evaluate what advantages and disadvantages small models have compared to large models, and whether they can be optimized for specific tasks. Through this analysis, we aim to provide useful insights for developers and researchers in selecting and utilizing LLMs.