1 citations · 3 across the 10 of their papers we have counts for
10 papers
Assessing Adversarial Robustness of Latent Reasoning Models
Shaolong Chen, Ang Li, Mingjie Li +1
Large language models increasingly rely on long chain-of-thought (CoT) trajectories for complex reasoning, but autoregressive generation brings substantial memory and inference cos…
LANPO: Bootstrapping Language and Numerical Feedback for Reinforcement Learning in LLMs
Ang Li, Yifei Wang, Zhihang Yuan +2
Reinforcement learning in large language models (LLMs) often relies on scalar rewards, a practice that discards valuable textual rationale buried in the rollouts, forcing the model…
Know When to Explore: Difficulty-Aware Certainty as a Guide for LLM Reinforcement Learning
Ang Li, Zhihang Yuan, Yang Zhang +2
Reinforcement Learning with Verifiable Feedback (RLVF) has become a key technique for enhancing the reasoning abilities of Large Language Models (LLMs). However, its reliance on sp…
Scalable Defense against In-the-wild Jailbreaking Attacks with Safety Context Retrieval
Taiye Chen, Zeming Wei, Ang Li +1
Large Language Models (LLMs) are known to be vulnerable to jailbreaking attacks, wherein adversaries exploit carefully engineered prompts to induce harmful or unethical responses.…
G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning
Xiaojun Guo, Ang Li, Yifei Wang +2
Although Large Language Models (LLMs) have demonstrated remarkable progress, their proficiency in graph-related tasks remains notably limited, hindering the development of truly ge…
X-Guard: Multilingual Guard Agent for Content Moderation
Bibek Upadhayay, Vahid Behzadan, Ph. D
Large Language Models (LLMs) have rapidly become integral to numerous applications in critical domains where reliability is paramount. Despite significant advances in safety framew…
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…
TERD: A Unified Framework for Safeguarding Diffusion Models Against Backdoors
Yichuan Mo, Hui Huang, Mingjie Li +2
Diffusion models have achieved notable success in image generation, but they remain highly vulnerable to backdoor attacks, which compromise their integrity by producing specific un…
PID: Prompt-Independent Data Protection Against Latent Diffusion Models
Ang Li, Yichuan Mo, Mingjie Li +1
The few-shot fine-tuning of Latent Diffusion Models (LDMs) has enabled them to grasp new concepts from a limited number of images. However, given the vast amount of personal images…
Adversarial Examples Are Not Real Features
Ang Li, Yifei Wang, Yiwen Guo +1
The existence of adversarial examples has been a mystery for years and attracted much interest. A well-known theory by \citet{ilyas2019adversarial} explains adversarial vulnerabili…