Small LLM

Small Language Models (LLMs) are a burgeoning area of research focusing on developing efficient and effective language models with significantly reduced computational requirements compared to their larger counterparts. Current research emphasizes improving their reasoning abilities through techniques like self-play mutual reasoning and enhancing their performance on specific tasks via methods such as logistic regression on embeddings and specialized finetuning strategies like LoRA. This work is significant because it addresses the accessibility, cost, and explainability limitations of large LLMs, potentially democratizing access to powerful language technologies and enabling their deployment in resource-constrained environments.