2.3k citations · 4.7k across the 64 of their papers we have counts for
6 papers · 1 filter
RAFT: Adapting Language Model to Domain Specific RAG
Tianjun Zhang, Shishir G. Patil, Naman Jain +4
Pretraining Large Language Models (LLMs) on large corpora of textual data is now a standard paradigm. When using these LLMs for many downstream applications, it is common to additi…
Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP
Omar Khattab, Keshav Santhanam, Xiang Lisa Li +4
Retrieval-augmented in-context learning has emerged as a powerful approach for addressing knowledge-intensive tasks using frozen language models (LM) and retrieval models (RM). Exi…
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…
Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM
Deepak Narayanan, Mohammad Shoeybi, Jared Casper +9
Large language models have led to state-of-the-art accuracies across a range of tasks. However, training these models efficiently is challenging for two reasons: a) GPU memory capa…
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…
Relevance-guided Supervision for OpenQA with ColBERT
Omar Khattab, Christopher Potts, Matei Zaharia
Systems for Open-Domain Question Answering (OpenQA) generally depend on a retriever for finding candidate passages in a large corpus and a reader for extracting answers from those…