37 citations · 40 across the 19 of their papers we have counts for
6 papers · 1 filter
CausalDiff: Causality-Inspired Disentanglement via Diffusion Model for Adversarial Defense
Mingkun Zhang, Keping Bi, Wei Chen +3
Despite ongoing efforts to defend neural classifiers from adversarial attacks, they remain vulnerable, especially to unseen attacks. In contrast, humans are difficult to be cheated…
Generative Retrieval Meets Multi-Graded Relevance
Yubao Tang, Ruqing Zhang, Jiafeng Guo +3
Generative retrieval represents a novel approach to information retrieval. It uses an encoder-decoder architecture to directly produce relevant document identifiers (docids) for qu…
Classifier Guidance Enhances Diffusion-based Adversarial Purification by Preserving Predictive Information
Mingkun Zhang, Jianing Li, Wei Chen +2
Adversarial purification is one of the promising approaches to defend neural networks against adversarial attacks. Recently, methods utilizing diffusion probabilistic models have a…
Evaluating Implicit Bias in Large Language Models by Attacking From a Psychometric Perspective
Yuchen Wen, Keping Bi, Wei Chen +2
As large language models (LLMs) become an important way of information access, there have been increasing concerns that LLMs may intensify the spread of unethical content, includin…
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…
A Unified Causal View of Instruction Tuning
Lu Chen, Wei Huang, Ruqing Zhang +3
Instruction tuning on a mixture of tasks has improved zero-shot capabilities in natural language processing (NLP). Nevertheless, existing methods often learn features that exhibit…