2 citations · 2 across the 16 of their papers we have counts for
18 papers
Membership Inference in Fine-tuned Diffusion Language Models via Token-level Memorization Asymmetry
Shengfang Zhai, Leo Marchyok, Yuling Shi +4
Diffusion language models (DLMs) have recently emerged as an alternative modeling paradigm to autoregressive LMs, offering advantages such as parallel generation and bidirectional…
UniNDM: A Unified Noise-driven Detection and Mitigation Framework Against Sexual Content in Text-to-Image Generation
Yao Huang, Yitong Sun, Huanran Chen +8
Despite the impressive generative capabilities of text-to-image diffusion models, they remain vulnerable to implicit sexual prompts, where subtle cues disguised as benign terms or…
Reinforcement Learning from Denoising Feedback
Qi He, Huan Chen, Ya Guo +3
Policy loss estimation remains a fundamental and long-standing challenge in reinforcement learning (RL) for diffusion language models (DLMs). We introduce Reinforcement Learning fr…
Nexus: Same Pretraining Loss, Better Downstream Generalization via Common Minima
Huanran Chen, Huaqing Zhang, Xiao Li +3
The foundational capabilities of large language models are acquired during pretraining on internet-scale, highly heterogeneous data mixtures. In this work, we investigate an intere…
Alignment Dynamics in LLM Fine-Tuning
Yuhan Huang, Huanran Chen, Yinpeng Dong
Although Large Language Models (LLMs) achieve strong alignment through supervised fine-tuning and reinforcement learning from human feedback, the alignment is often fragile under s…
AgentWorm: Self-Propagating Attacks Across LLM Agent Ecosystems
Yihao Zhang, Zeming Wei, Xiaokun Luan +7
Autonomous LLM-based agents increasingly operate as long-running processes forming densely interconnected multi-agent ecosystems, whose security properties remain largely unexplore…
NDM: A Noise-driven Detection and Mitigation Framework against Implicit Sexual Intentions in Text-to-Image Generation
Yitong Sun, Yao Huang, Ruochen Zhang +4
Despite the impressive generative capabilities of text-to-image (T2I) diffusion models, they remain vulnerable to generating inappropriate content, especially when confronted with…
Diffusion Models as Dataset Distillation Priors
Duo Su, Huyu Wu, Huanran Chen +4
Dataset distillation aims to synthesize compact yet informative datasets from large ones. A significant challenge in this field is achieving a trifecta of diversity, generalization…
Reliable Unlearning Harmful Information in LLMs with Metamorphosis Representation Projection
Chengcan Wu, Zeming Wei, Huanran Chen +2
While Large Language Models (LLMs) have demonstrated impressive performance in various domains and tasks, concerns about their safety are becoming increasingly severe. In particula…
Unveiling Trust in Multimodal Large Language Models: Evaluation, Analysis, and Mitigation
Yichi Zhang, Yao Huang, Yifan Wang +10
The trustworthiness of Multimodal Large Language Models (MLLMs) remains an intense concern despite the significant progress in their capabilities. Existing evaluation and mitigatio…
Mitigating Overthinking in Large Reasoning Models via Manifold Steering
Yao Huang, Huanran Chen, Shouwei Ruan +3
Recent advances in Large Reasoning Models (LRMs) have demonstrated remarkable capabilities in solving complex tasks such as mathematics and coding. However, these models frequently…
Unveiling the Basin-Like Loss Landscape in Large Language Models
Huanran Chen, Yinpeng Dong, Zeming Wei +4
We discover the emergence of \textit{basins} in the loss landscape of large language models. As model scale increases, LLMs become progressively more resilient to random perturbati…
Efficient Input-level Backdoor Defense on Text-to-Image Synthesis via Neuron Activation Variation
Shengfang Zhai, Jiajun Li, Yue Liu +7
In recent years, text-to-image (T2I) diffusion models have gained significant attention for their ability to generate high quality images reflecting text prompts. However, their gr…
Towards the Worst-case Robustness of Large Language Models
Huanran Chen, Yinpeng Dong, Zeming Wei +2
Recent studies have revealed the vulnerability of large language models to adversarial attacks, where adversaries craft specific input sequences to induce harmful, violent, private…
Scaling Laws for Black box Adversarial Attacks
Chuan Liu, Huanran Chen, Yichi Zhang +2
Adversarial examples exhibit cross-model transferability, enabling threatening black-box attacks on commercial models. Model ensembling, which attacks multiple surrogate models, is…
ADBM: Adversarial diffusion bridge model for reliable adversarial purification
Xiao Li, Wenxuan Sun, Huanran Chen +5
Recently Diffusion-based Purification (DiffPure) has been recognized as an effective defense method against adversarial examples. However, we find DiffPure which directly employs t…
MultiTrust: A Comprehensive Benchmark Towards Trustworthy Multimodal Large Language Models
Yichi Zhang, Yao Huang, Yitong Sun +10
Despite the superior capabilities of Multimodal Large Language Models (MLLMs) across diverse tasks, they still face significant trustworthiness challenges. Yet, current literature…
Membership Inference on Text-to-Image Diffusion Models via Conditional Likelihood Discrepancy
Shengfang Zhai, Huanran Chen, Yinpeng Dong +5
Text-to-image diffusion models have achieved tremendous success in the field of controllable image generation, while also coming along with issues of privacy leakage and data copyr…