The discussion delves into the nuances of data usage in language models, emphasizing the importance of data quality and its impact on model performance. Irwan highlights the misalignment between the training objectives of large language models and their intended applications, pointing out that while they excel at many tasks, they struggle with following user instructions. Efforts to fine-tune these models for better alignment with human intent are also explored.
