Exploration of the Efficiency of SLM-Enabled Platforms for Everyday Tasks

Abstract

This study explores the potential of Small Language Models (SLMs) as an efficient and secure alternative to larger models like GPT-4 for various natural language processing (NLP) tasks. With growing concerns around data privacy and the resource-intensiveness of large models, SLMs present a promising solution for research and applications requiring fast, cost-effective, and locally deployable models. The research evaluates several SLMs across tasks such as translation, summarization, Named Entity Recognition (NER), text generation, classification, and retrieval-augmented generation (RAG), comparing their performance against larger counterparts. Models were assessed using a range of metrics specific to the intended task. Results show that smaller models perform well on complex tasks, often rivalling or even outperforming larger models like Phi-3.5. The study concludes that SLMs offer an optimal trade-off between performance and computational efficiency, particularly in environments where data security and resource constraints are critical. The findings highlight the growing viability of smaller models for a wide range of real-world applications.