LLM Deployment

Deploying large language models (LLMs) efficiently and responsibly is a major focus of current research, driven by the models' high computational demands and potential for errors. Key areas of investigation include optimizing energy efficiency during inference, developing methods for model compression and faster inference, and creating techniques for enhancing model accountability and transparency, such as metacognitive approaches for error detection and correction. These efforts aim to make LLMs more accessible, sustainable, and trustworthy for a wider range of applications, impacting both scientific advancements and practical deployment across various sectors.