Language Model Representation

Language model representation research focuses on understanding and improving how language models encode information, aiming to enhance interpretability, accuracy, and fairness. Current efforts explore methods to incorporate temporal context into model training, using novel architectures and pre-training objectives to capture evolving word meanings and time-sensitive information. This work is significant because it addresses limitations in existing models, leading to improved performance on various downstream tasks, including those requiring temporal reasoning and the mitigation of biases in model outputs. Furthermore, improved interpretability of these representations is a key goal, allowing for better understanding of how these powerful models function.