Large language models (LLMs) have shown impressive performance on complex reasoning
by leveraging chain-of-thought (CoT) prompting to generate intermediate reasoning chains …

Neural compression is the application of neural networks and other machine learning
methods to data compression. Recent advances in statistical machine learning have opened …

The stunning qualitative improvement of text-to-image models has led to their widespread
attention and adoption. However, we lack a comprehensive quantitative understanding of …

Generative language models define distributions over sequences of tokens that can
represent essentially any combination of data modalities (eg, any permutation of image …

Language models (LMs) often struggle to pay enough attention to the input context, and
generate texts that are unfaithful or contain hallucinations. To mitigate this issue, we present …

Transfer learning has recently become the dominant paradigm of machine learning. Pre-
trained models fine-tuned for downstream tasks achieve better performance with fewer …

We introduce REPLUG, a retrieval-augmented language modeling framework that treats the
language model (LM) as a black box and augments it with a tuneable retrieval model. Unlike …