Language Model Performance

Research on large language model (LLM) performance focuses on improving accuracy, efficiency, and mitigating biases. Current efforts involve analyzing the relationship between training data density and model predictions, exploring methods to decompose model architectures for enhanced performance and bias reduction, and developing techniques to predict model performance on unseen tasks. These investigations, often using transformer-based models, aim to provide a deeper understanding of LLM behavior and lead to more reliable and responsible AI systems with improved practical applications.