Thelimited datainbatchRLproduces inherent uncertainty in value estimates of states/actions that were insufficiently represented in the training data.

In this work, we introduce ChatQA, a suite of models that outperform GPT -4 on retrieval-augmented generation (RAG) and conversational question answering (QA).

Transformer-based models, which have recently seen a surge in popularity due to their good performance and applicability to a variety of tasks, have a similar problem.

We demonstrate qualitatively and quantitatively that our proposed approach is able tomodel the appearance ofindividual strokes,aswell asthe compositional structure oflargerdiagram drawings.

The resulting features are evaluated on k-nearest neighbor classification over 11 datasets from vision, 5 from natural language processing, and 2 from audio.