Language Model Application

Large language model (LLM) applications are rapidly expanding, focusing on improving efficiency and mitigating biases in various domains. Current research emphasizes techniques like prompt engineering and specialized model fine-tuning to enhance performance and address limitations such as context window size and memory constraints, as well as developing frameworks for assessing and mitigating bias in LLM outputs. This work is crucial for responsible deployment of LLMs across diverse applications, from healthcare to general-purpose chatbots, ensuring both effectiveness and ethical considerations are addressed.