Language Model Fine Tuning

Fine-tuning pre-trained language models (LLMs) adapts these powerful models to specific downstream tasks, improving performance and addressing limitations like factual inaccuracies or biases. Current research emphasizes efficient fine-tuning techniques, including methods that reduce computational costs (e.g., using adapters or low-rank adaptations) and enhance privacy (e.g., through differential privacy). These advancements are crucial for broadening LLM applications across diverse fields, from scientific writing assistance to drug discovery, while mitigating risks associated with their deployment.