2.3k citations · 2.8k across the 30 of their papers we have counts for
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ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction
Keshav Santhanam, Omar Khattab, Jon Saad-Falcon +2
Neural information retrieval (IR) has greatly advanced search and other knowledge-intensive language tasks. While many neural IR methods encode queries and documents into single-ve…
Hindsight: Posterior-guided training of retrievers for improved open-ended generation
Ashwin Paranjape, Omar Khattab, Christopher Potts +2
Many text generation systems benefit from using a retriever to retrieve passages from a textual knowledge corpus (e.g., Wikipedia) which are then provided as additional context to…
On the Opportunities and Risks of Foundation Models
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli +111
AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.…
Learning Passage Impacts for Inverted Indexes
Antonio Mallia, Omar Khattab, Nicola Tonellotto +1
Neural information retrieval systems typically use a cascading pipeline, in which a first-stage model retrieves a candidate set of documents and one or more subsequent stages re-ra…
Baleen: Robust Multi-Hop Reasoning at Scale via Condensed Retrieval
Omar Khattab, Christopher Potts, Matei Zaharia
Multi-hop reasoning (i.e., reasoning across two or more documents) is a key ingredient for NLP models that leverage large corpora to exhibit broad knowledge. To retrieve evidence p…