Pre Trained Large Language Model

Pre-trained large language models (LLMs) are massive neural networks trained on vast text datasets, aiming to generate human-quality text and perform various language tasks. Current research focuses on improving their efficiency (e.g., through parameter-efficient fine-tuning, low-rank decomposition, and federated learning), addressing privacy concerns (e.g., via federated learning and proxy tuning), and enhancing their capabilities for specific domains (e.g., via knowledge injection and prompt engineering). These advancements are significant because they increase the accessibility and applicability of LLMs across diverse fields, from biomedical question answering to time series analysis, while simultaneously mitigating computational and ethical challenges.