Two popular approaches for customizing large language models (LLMs) for downstream tasks are fine-tuning and in-context learning (ICL). In a recent study, researchers at Google DeepMind and Stanford ...

MIT and IBM released ChartNet, a 1.7-million-sample synthetic training dataset that lets compact open-source vision-language ...

How does one judge whether a model or a set of models and their results are adequate for supporting regulatory decision making? The essence of the problem is whether the behavior of a model matches ...