8 citations · 16 across the 5 of their papers we have counts for
5 papers
Disclosure and Mitigation of Gender Bias in LLMs
Xiangjue Dong, Yibo Wang, Philip S. Yu +1
Large Language Models (LLMs) can generate biased responses. Yet previous direct probing techniques contain either gender mentions or predefined gender stereotypes, which are challe…
Probing Explicit and Implicit Gender Bias through LLM Conditional Text Generation
Xiangjue Dong, Yibo Wang, Philip S. Yu +1
Large Language Models (LLMs) can generate biased and toxic responses. Yet most prior work on LLM gender bias evaluation requires predefined gender-related phrases or gender stereot…
CoPT: Mitigating Bias in Pre-trained Language Models through Counterfactual Contrastive Prompt Tuning
Xiangjue Dong, Ziwei Zhu, Zhuoer Wang +2
Pre-trained Language Models are widely used in many important real-world applications. However, recent studies show that these models can encode social biases from large pre-traini…
Everything Perturbed All at Once: Enabling Differentiable Graph Attacks
Haoran Liu, Bokun Wang, Jianling Wang +3
As powerful tools for representation learning on graphs, graph neural networks (GNNs) have played an important role in applications including social networks, recommendation system…
PromptAttack: Probing Dialogue State Trackers with Adversarial Prompts
Xiangjue Dong, Yun He, Ziwei Zhu +1
A key component of modern conversational systems is the Dialogue State Tracker (or DST), which models a user's goals and needs. Toward building more robust and reliable DSTs, we in…