117 citations · 529 across the 70 of their papers we have counts for
11 papers · 1 filter
RIGHT: Retrieval-augmented Generation for Mainstream Hashtag Recommendation
Run-Ze Fan, Yixing Fan, Jiangui Chen +3
Automatic mainstream hashtag recommendation aims to accurately provide users with concise and popular topical hashtags before publication. Generally, mainstream hashtag recommendat…
Perturbation-Invariant Adversarial Training for Neural Ranking Models: Improving the Effectiveness-Robustness Trade-Off
Yu-An Liu, Ruqing Zhang, Mingkun Zhang +4
Neural ranking models (NRMs) have shown great success in information retrieval (IR). But their predictions can easily be manipulated using adversarial examples, which are crafted b…
CAME: Competitively Learning a Mixture-of-Experts Model for First-stage Retrieval
Yinqiong Cai, Yixing Fan, Keping Bi +4
The first-stage retrieval aims to retrieve a subset of candidate documents from a huge collection both effectively and efficiently. Since various matching patterns can exist betwee…
From Relevance to Utility: Evidence Retrieval with Feedback for Fact Verification
Hengran Zhang, Ruqing Zhang, Jiafeng Guo +3
Retrieval-enhanced methods have become a primary approach in fact verification (FV); it requires reasoning over multiple retrieved pieces of evidence to verify the integrity of a c…
Inducing Causal Structure for Abstractive Text Summarization
Lu Chen, Ruqing Zhang, Wei Huang +3
The mainstream of data-driven abstractive summarization models tends to explore the correlations rather than the causal relationships. Among such correlations, there can be spuriou…
Black-box Adversarial Attacks against Dense Retrieval Models: A Multi-view Contrastive Learning Method
Yu-An Liu, Ruqing Zhang, Jiafeng Guo +4
Neural ranking models (NRMs) and dense retrieval (DR) models have given rise to substantial improvements in overall retrieval performance. In addition to their effectiveness, and m…
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…
On the Robustness of Generative Retrieval Models: An Out-of-Distribution Perspective
Yu-An Liu, Ruqing Zhang, Jiafeng Guo +2
Recently, we have witnessed generative retrieval increasingly gaining attention in the information retrieval (IR) field, which retrieves documents by directly generating their iden…
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…
Topic-oriented Adversarial Attacks against Black-box Neural Ranking Models
Yu-An Liu, Ruqing Zhang, Jiafeng Guo +4
Neural ranking models (NRMs) have attracted considerable attention in information retrieval. Unfortunately, NRMs may inherit the adversarial vulnerabilities of general neural netwo…
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…