Search Results for author: Omar Khattab

Found 9 papers, 6 papers with code

PLAID: An Efficient Engine for Late Interaction Retrieval

1 code implementation19 May 2022 Keshav Santhanam, Omar Khattab, Christopher Potts, Matei Zaharia

PLAID uses centroid interaction as well as centroid pruning, a mechanism for sparsifying the bag of centroids, within a highly-optimized engine to reduce late interaction search latency by up to 7$\times$ on a GPU and 45$\times$ on a CPU against vanilla ColBERTv2, while continuing to deliver state-of-the-art retrieval quality.

Information Retrieval

Introducing Neural Bag of Whole-Words with ColBERTer: Contextualized Late Interactions using Enhanced Reduction

no code implementations24 Mar 2022 Sebastian Hofstätter, Omar Khattab, Sophia Althammer, Mete Sertkan, Allan Hanbury

Recent progress in neural information retrieval has demonstrated large gains in effectiveness, while often sacrificing the efficiency and interpretability of the neural model compared to classical approaches.

Information Retrieval

Hindsight: Posterior-guided training of retrievers for improved open-ended generation

no code implementations ICLR 2022 Ashwin Paranjape, Omar Khattab, Christopher Potts, Matei Zaharia, Christopher D. Manning

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 the generator.

Text Generation

On the Opportunities and Risks of Foundation Models

no code implementations16 Aug 2021 Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, Shyamal Buch, Dallas Card, Rodrigo Castellon, Niladri Chatterji, Annie Chen, Kathleen Creel, Jared Quincy Davis, Dora Demszky, Chris Donahue, Moussa Doumbouya, Esin Durmus, Stefano Ermon, John Etchemendy, Kawin Ethayarajh, Li Fei-Fei, Chelsea Finn, Trevor Gale, Lauren Gillespie, Karan Goel, Noah Goodman, Shelby Grossman, Neel Guha, Tatsunori Hashimoto, Peter Henderson, John Hewitt, Daniel E. Ho, Jenny Hong, Kyle Hsu, Jing Huang, Thomas Icard, Saahil Jain, Dan Jurafsky, Pratyusha Kalluri, Siddharth Karamcheti, Geoff Keeling, Fereshte Khani, Omar Khattab, Pang Wei Kohd, Mark Krass, Ranjay Krishna, Rohith Kuditipudi, Ananya Kumar, Faisal Ladhak, Mina Lee, Tony Lee, Jure Leskovec, Isabelle Levent, Xiang Lisa Li, Xuechen Li, Tengyu Ma, Ali Malik, Christopher D. Manning, Suvir Mirchandani, Eric Mitchell, Zanele Munyikwa, Suraj Nair, Avanika Narayan, Deepak Narayanan, Ben Newman, Allen Nie, Juan Carlos Niebles, Hamed Nilforoshan, Julian Nyarko, Giray Ogut, Laurel Orr, Isabel Papadimitriou, Joon Sung Park, Chris Piech, Eva Portelance, Christopher Potts, aditi raghunathan, Rob Reich, Hongyu Ren, Frieda Rong, Yusuf Roohani, Camilo Ruiz, Jack Ryan, Christopher Ré, Dorsa Sadigh, Shiori Sagawa, Keshav Santhanam, Andy Shih, Krishnan Srinivasan, Alex Tamkin, Rohan Taori, Armin W. Thomas, Florian Tramèr, Rose E. Wang, William Wang, Bohan Wu, Jiajun Wu, Yuhuai Wu, Sang Michael Xie, Michihiro Yasunaga, Jiaxuan You, Matei Zaharia, Michael Zhang, Tianyi Zhang, Xikun Zhang, Yuhui Zhang, Lucia Zheng, Kaitlyn Zhou, Percy Liang

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.

Transfer Learning

Learning Passage Impacts for Inverted Indexes

1 code implementation24 Apr 2021 Antonio Mallia, Omar Khattab, Nicola Tonellotto, Torsten Suel

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-rank this set using contextualized language models such as BERT.

Information Retrieval Language Modelling +1

Baleen: Robust Multi-Hop Reasoning at Scale via Condensed Retrieval

1 code implementation NeurIPS 2021 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.

Question Answering

Relevance-guided Supervision for OpenQA with ColBERT

3 code implementations1 Jul 2020 Omar Khattab, Christopher Potts, Matei Zaharia

In much recent work, the retriever is a learned component that uses coarse-grained vector representations of questions and passages.

Open-Domain Question Answering

ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT

3 code implementations27 Apr 2020 Omar Khattab, Matei Zaharia

ColBERT introduces 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.

Document Ranking Information Retrieval +2

Cannot find the paper you are looking for? You can Submit a new open access paper.