6 papers
TrustLDM: Benchmarking Trustworthiness in Language Diffusion Models
Yichuan Mo, Yukun Jiang, Yanbo Shi +4
The rapid development of Language Diffusion Models (LDMs) challenges the dominant position of auto-regressive competitors in language processing. However, their flexible, any-order…
On the Adversarial Transferability of Generalized "Skip Connections"
Yisen Wang, Yichuan Mo, Dongxian Wu +3
Skip connection is an essential ingredient for modern deep models to be deeper and more powerful. Despite their huge success in normal scenarios (state-of-the-art classification pe…
Finding and Reactivating Post-Trained LLMs' Hidden Safety Mechanisms
Mingjie Li, Wai Man Si, Michael Backes +2
Despite the impressive performance of general-purpose large language models (LLMs), they often require fine-tuning or post-training to excel at specific tasks. For instance, large…
Decoding Large Language Diffusion Models with Foreseeing Movement
Yichuan Mo, Quan Chen, Mingjie Li +2
Large Language Diffusion Models (LLDMs) benefit from a flexible decoding mechanism that enables parallelized inference and controllable generations over autoregressive models. Yet…
Are Smarter LLMs Safer? Exploring Safety-Reasoning Trade-offs in Prompting and Fine-Tuning
Ang Li, Yichuan Mo, Mingjie Li +2
Large Language Models (LLMs) have demonstrated remarkable success across various NLP benchmarks. However, excelling in complex tasks that require nuanced reasoning and precise deci…
MADE: Graph Backdoor Defense with Masked Unlearning
Xiao Lin, Mingjie Li, Yisen Wang
Graph Neural Networks (GNNs) have garnered significant attention from researchers due to their outstanding performance in handling graph-related tasks, such as social network analy…