Fine Tuned Large Language Model

Fine-tuning large language models (LLMs) adapts pre-trained models to specific tasks by adjusting a subset of their parameters, improving efficiency and performance compared to training from scratch. Current research focuses on optimizing fine-tuning strategies, including exploring efficient parameter updates (e.g., targeting specific attention mechanism matrices or using low-rank adaptation), developing methods for selective knowledge unlearning, and enhancing model performance through techniques like ensemble methods and prompt engineering. These advancements are significantly impacting various fields, enabling improved performance in tasks ranging from medical evidence summarization and depression detection to code generation and relation extraction, while also addressing challenges like resource constraints and model calibration.