Our paper at EMNLP 2023, “Learning Interpretable Style Embeddings via Prompting LLMs” used GPT-3 to generate a synthetic dataset used for training interpretable text style embeddings, where each dimension of the embedding vector has a meaningful interpretation to a human.

Our paper at ICLR 2023, “Bidirectional Language Models Are Also Few-shot Learners” surprisingly discovers that older models like T5, that predate GPT-3, were promptable and could perform in-context learning.