Lightweight Large Language Model

Lightweight Large Language Models (LLMs) aim to deliver the capabilities of larger LLMs with significantly reduced computational demands, enabling deployment on resource-constrained devices like smartphones and edge servers. Current research focuses on optimizing existing architectures (like BERT and Transformer variants) for efficiency, developing novel training techniques to improve performance despite smaller parameter counts, and exploring methods for calibration and emotion-aware retrofitting. This area is crucial for expanding LLM accessibility, enhancing privacy by enabling on-device processing, and facilitating applications in diverse fields such as healthcare, robotics, and phishing detection where real-time performance and limited resources are critical.