Omar Khattab

TL;DR: ColBERT is presented, a novel ranking model that adapts deep LMs (in particular, BERT) for efficient retrieval that is competitive with existing BERT-based models (and outperforms every non-BERT baseline) and enables leveraging vector-similarity indexes for end-to-end retrieval directly from millions of documents.

TL;DR: DeepImpact as mentioned in this paper leverages DocT5Query to enrich the document collection and, using a contextualized language model, directly estimates the semantic importance of tokens in a document, producing a single-value representation for each token in each document.

TL;DR: The authors provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles(e. g.g. model architectures, training procedures, data, systems, security, evaluation, theory) to their applications.

TL;DR: This work proposes a weak supervision strategy that iteratively uses ColBERT to create its own training data, which greatly improves OpenQA retrieval on both Natural Questions and TriviaQA, and the resulting end-to-end Open QA system attains state-of-the-art performance on both of those datasets.

TL;DR: This paper proposed ColBERT, a novel ranking model that adapts deep LMs (in particular, BERT) for efficient retrieval by introducing a late interaction architecture that independently encodes the query and the document using BERT and then employs a cheap yet powerful interaction step that models their fine-grained similarity.