54 citations · 126 across the 7 of their papers we have counts for
11 papers
Retrieval as Attention: End-to-end Learning of Retrieval and Reading within a Single Transformer
Zhengbao Jiang, Luyu Gao, Jun Araki +4
Systems for knowledge-intensive tasks such as open-domain question answering (QA) usually consist of two stages: efficient retrieval of relevant documents from a large corpus and d…
Tevatron: An Efficient and Flexible Toolkit for Dense Retrieval
Luyu Gao, Xueguang Ma, Jimmy Lin +1
Recent rapid advancements in deep pre-trained language models and the introductions of large datasets have powered research in embedding-based dense retrieval. While several good r…
Unsupervised Corpus Aware Language Model Pre-training for Dense Passage Retrieval
Luyu Gao, Jamie Callan
Recent research demonstrates the effectiveness of using fine-tuned language models~(LM) for dense retrieval. However, dense retrievers are hard to train, typically requiring heavil…
COIL: Revisit Exact Lexical Match in Information Retrieval with Contextualized Inverted List
Luyu Gao, Zhuyun Dai, Jamie Callan
Classical information retrieval systems such as BM25 rely on exact lexical match and carry out search efficiently with inverted list index. Recent neural IR models shifts towards s…
Condenser: a Pre-training Architecture for Dense Retrieval
Luyu Gao, Jamie Callan
Pre-trained Transformer language models (LM) have become go-to text representation encoders. Prior research fine-tunes deep LMs to encode text sequences such as sentences and passa…
Rethink Training of BERT Rerankers in Multi-Stage Retrieval Pipeline
Luyu Gao, Zhuyun Dai, Jamie Callan
Pre-trained deep language models~(LM) have advanced the state-of-the-art of text retrieval. Rerankers fine-tuned from deep LM estimates candidate relevance based on rich contextual…