63 citations · 167 across the 8 of their papers we have counts for
5 papers · 1 filter
CorpusBrain++: A Continual Generative Pre-Training Framework for Knowledge-Intensive Language Tasks
Jiafeng Guo, Changjiang Zhou, Ruqing Zhang +4
Knowledge-intensive language tasks (KILTs) typically require retrieving relevant documents from trustworthy corpora, e.g., Wikipedia, to produce specific answers. Very recently, a…
Continual Learning for Generative Retrieval over Dynamic Corpora
Jiangui Chen, Ruqing Zhang, Jiafeng Guo +4
Generative retrieval (GR) directly predicts the identifiers of relevant documents (i.e., docids) based on a parametric model. It has achieved solid performance on many ad-hoc retri…
Semantic-Enhanced Differentiable Search Index Inspired by Learning Strategies
Yubao Tang, Ruqing Zhang, Jiafeng Guo +5
Recently, a new paradigm called Differentiable Search Index (DSI) has been proposed for document retrieval, wherein a sequence-to-sequence model is learned to directly map queries…
A Unified Generative Retriever for Knowledge-Intensive Language Tasks via Prompt Learning
Jiangui Chen, Ruqing Zhang, Jiafeng Guo +4
Knowledge-intensive language tasks (KILTs) benefit from retrieving high-quality relevant contexts from large external knowledge corpora. Learning task-specific retrievers that retu…
FedMatch: Federated Learning Over Heterogeneous Question Answering Data
Jiangui Chen, Ruqing Zhang, Jiafeng Guo +2
Question Answering (QA), a popular and promising technique for intelligent information access, faces a dilemma about data as most other AI techniques. On one hand, modern QA method…