AIhub monthly digest: April 2023 – addressing class imbalance, personalized reward functions, and ad hoc teamwork

Welcome to our April 2023 monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, find out about recent events, and more. This month, we learn how to address class imbalance in natural language processing, investigate personalized reward functions, and put together a list of large language model resources. Class imbalance in training and evaluation datasets can pose a challenge for natural language processing (NLP) models, which are more heavily influenced by majority class data during training. As a result, NLP models tend to perform poorly on the minority classes, which often contain the cases that are most interesting to the downstream user. In this blogpost, Sophie Henning and Annemarie Friedrich give an overview of such class imbalance and survey methods for addressing it.