activity
20222025
most citedCorpusBrain: Pre-train a Generative Retrieval Model for Knowledge-Intensive Language Tasks

55 citations · 120 across the 19 of their papers we have counts for

collaborators

18 papers

cs.IR20242 cited

Robust Neural Information Retrieval: An Adversarial and Out-of-distribution Perspective

Yu-An Liu, Ruqing Zhang, Jiafeng Guo +3

Recent advances in neural information retrieval (IR) models have significantly enhanced their effectiveness over various IR tasks. The robustness of these models, essential for ens…

cs.IR2024

Bootstrapped Pre-training with Dynamic Identifier Prediction for Generative Retrieval

Yubao Tang, Ruqing Zhang, Jiafeng Guo +3

Generative retrieval uses differentiable search indexes to directly generate relevant document identifiers in response to a query. Recent studies have highlighted the potential of…

cs.IR2024

Robust Information Retrieval

Yu-An Liu, Ruqing Zhang, Jiafeng Guo +1

Beyond effectiveness, the robustness of an information retrieval (IR) system is increasingly attracting attention. When deployed, a critical technology such as IR should not only d…

cs.AI20247 cited

Applications of Explainable artificial intelligence in Earth system science

Feini Huang, Shijie Jiang, Lu Li +7

In recent years, artificial intelligence (AI) rapidly accelerated its influence and is expected to promote the development of Earth system science (ESS) if properly harnessed. In a…

cs.IR20241 cited

Multi-granular Adversarial Attacks against Black-box Neural Ranking Models

Yu-An Liu, Ruqing Zhang, Jiafeng Guo +3

Adversarial ranking attacks have gained increasing attention due to their success in probing vulnerabilities, and, hence, enhancing the robustness, of neural ranking models. Conven…

cs.IR20241 cited

Listwise Generative Retrieval Models via a Sequential Learning Process

Yubao Tang, Ruqing Zhang, Jiafeng Guo +3

Recently, a novel generative retrieval (GR) paradigm has been proposed, where a single sequence-to-sequence model is learned to directly generate a list of relevant document identi…