Exploring the Landscape of Large and Small Language Models: Advancements, Trade-offs, and Future Directions

Recent advances in natural language processing (NLP) have led to the development of large language models (LLMs) and small language models (small LMs), which have revolutionized the field. LLMs, such as GPT-3 and PaLM, are capable of performing a wide range of tasks with state-of-the-art accuracy, thanks to their vast number of parameters and extensive training data. However, these models are resource-intensive, requiring significant computational power for both training and deployment. In contrast, small LMs offer a more efficient alternative, with reduced computational requirements and faster inference times, making them well-suited for resource-constrained environments such as mobile devices and real-time applications. This survey explores the key differences between LLMs and small LMs, focusing on aspects such as model size, computational efficiency, performance, and deployment scenarios. We also discuss the trade-offs associated with selecting between the two, and highlight techniques such as knowledge distillation and model pruning that are used to optimize small LMs. Finally, we examine the future directions of language model research, including hybrid approaches that combine the strengths of both LLMs and small LMs, and advancements aimed at improving energy efficiency and sustainability. Our goal is to provide a comprehensive overview of the current landscape of LLMs and small LMs, and to offer insights into the ongoing challenges and opportunities in the field of NLP.