With the recent advances in deep learning, different approaches to improving pre-trained
language models (PLMs) have been proposed. PLMs have advanced state-of-the-art …

AI is widely thought to be poised to transform business, yet current perceptions of the scope
of this transformation may be myopic. Recent progress in natural language processing …

The rise in malicious usage of large language models, such as fake content creation and
academic plagiarism, has motivated the development of approaches that identify AI …

We present VideoCLIP, a contrastive approach to pre-train a unified model for zero-shot
video and text understanding, without using any labels on downstream tasks. VideoCLIP …

The recent" Text-to-Text Transfer Transformer"(T5) leveraged a unified text-to-text format and
scale to attain state-of-the-art results on a wide variety of English-language NLP tasks. In this …

Language models typically need to be trained or finetuned in order to acquire new
knowledge, which involves updating their weights. We instead envision language models …

Conducting text retrieval in a dense learned representation space has many intriguing
advantages over sparse retrieval. Yet the effectiveness of dense retrieval (DR) often requires …

Although pretrained language models can be fine-tuned to produce state-of-the-art results
for a very wide range of language understanding tasks, the dynamics of this process are not …

The goal of text ranking is to generate an ordered list of texts retrieved from a corpus in
response to a query. Although the most common formulation of text ranking is search …

Challenging problems such as open-domain question answering, fact checking, slot filling
and entity linking require access to large, external knowledge sources. While some models …