1 citations · 3 across the 7 of their papers we have counts for
7 papers
Revisiting Semi-supervised Adversarial Robustness via Noise-aware Online Robust Distillation
Tsung-Han Wu, Hung-Ting Su, Shang-Tse Chen +1
The robust self-training (RST) framework has emerged as a prominent approach for semi-supervised adversarial training. To explore the possibility of tackling more complicated tasks…
Step by Step to Fairness: Attributing Societal Bias in Task-oriented Dialogue Systems
Hsuan Su, Rebecca Qian, Chinnadhurai Sankar +4
Recent works have shown considerable improvements in task-oriented dialogue (TOD) systems by utilizing pretrained large language models (LLMs) in an end-to-end manner. However, the…
Learning from Red Teaming: Gender Bias Provocation and Mitigation in Large Language Models
Hsuan Su, Cheng-Chu Cheng, Hua Farn +4
Recently, researchers have made considerable improvements in dialogue systems with the progress of large language models (LLMs) such as ChatGPT and GPT-4. These LLM-based chatbots…
Position Matters! Empirical Study of Order Effect in Knowledge-grounded Dialogue
Hsuan Su, Shachi H Kumar, Sahisnu Mazumder +7
With the power of large pretrained language models, various research works have integrated knowledge into dialogue systems. The traditional techniques treat knowledge as part of th…
Fair Robust Active Learning by Joint Inconsistency
Tsung-Han Wu, Hung-Ting Su, Shang-Tse Chen +1
Fairness and robustness play vital roles in trustworthy machine learning. Observing safety-critical needs in various annotation-expensive vision applications, we introduce a novel…
Enhancing Targeted Attack Transferability via Diversified Weight Pruning
Hung-Jui Wang, Yu-Yu Wu, Shang-Tse Chen
Malicious attackers can generate targeted adversarial examples by imposing tiny noises, forcing neural networks to produce specific incorrect outputs. With cross-model transferabil…