Fine Tuned Language Model

Fine-tuning pre-trained language models (LLMs) adapts their general capabilities to specific tasks, improving performance and addressing limitations like poor reasoning or bias. Current research emphasizes enhancing reasoning abilities, mitigating biases, and improving model calibration and compatibility across updates, often employing techniques like parameter-efficient fine-tuning and model merging. This work is significant because it enables the creation of more reliable, specialized LLMs for diverse applications, ranging from clinical documentation to scientific knowledge base construction, while also addressing crucial concerns about model safety and privacy.