Large language models (LLMs) have come closest among all models to date to mastering
human language, yet opinions about their linguistic and cognitive capabilities remain split …

This survey explores how Deep Learning has battled the COVID-19 pandemic and provides
directions for future research on COVID-19. We cover Deep Learning applications in Natural …

Abstract Language models (LMs) exhibit remarkable abilities to solve new tasks from just a
few examples or textual instructions, especially at scale. They also, paradoxically, struggle …

Abstract Retrieval-Augmented Language Modeling (RALM) methods, which condition a
language model (LM) on relevant documents from a grounding corpus during generation …

Evaluating the factuality of long-form text generated by large language models (LMs) is non-
trivial because (1) generations often contain a mixture of supported and unsupported pieces …

This paper presents E5, a family of state-of-the-art text embeddings that transfer well to a
wide range of tasks. The model is trained in a contrastive manner with weak supervision …

Large language models (LLMs) show impressive abilities via few-shot prompting.
Commercialized APIs such as OpenAI GPT-3 further increase their use in real-world …

Large language models (LLMs) have emerged as a widely-used tool for information
seeking, but their generated outputs are prone to hallucination. In this work, our aim is to …

Knowledge-intensive tasks, such as open-domain question answering (QA), require access
to a large amount of world or domain knowledge. A common approach for knowledge …

We introduce INSTRUCTOR, a new method for computing text embeddings given task
instructions: every text input is embedded together with instructions explaining the use case …