Mark Chen

TL;DR: GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic.

TL;DR: This article showed that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches.

TL;DR: This work proposes a two-stage model: a prior that generates a CLIP image embedding given a text caption, and a decoder that generates an image conditioned on the imageembedding, and shows that explicitly generating image representations improves image diversity with minimal loss in photorealism and caption similarity.

TL;DR: This work describes a simple approach based on a transformer that autoregressively models the text and image tokens as a single stream of data that is competitive with previous domain-specific models when evaluated in a zero-shot fashion.

TL;DR: This work trains a sequence Transformer to auto-regressively predict pixels, without incorporating knowledge of the 2D input structure, and finds that a GPT-2 scale model learns strong image representations as measured by linear probing, fine-tuning, and low-data classification.