5 papers · 1 filter
Are LLM Uncertainty and Correctness Encoded by the Same Features? A Functional Dissociation via Sparse Autoencoders
Het Patel, Tiejin Chen, Hua Wei +2
Large language models can be uncertain yet correct, or confident yet wrong, raising the question of whether their output-level uncertainty and their actual correctness are driven b…
Conformal Feedback Alignment: Quantifying Answer-Level Reliability for Robust LLM Alignment
Tiejin Chen, Xiaoou Liu, Vishnu Nandam +2
Preference-based alignment like Reinforcement Learning from Human Feedback (RLHF) learns from pairwise preferences, yet the labels are often noisy and inconsistent. Existing uncert…
Zer0-Jack: A Memory-efficient Gradient-based Jailbreaking Method for Black-box Multi-modal Large Language Models
Tiejin Chen, Kaishen Wang, Hua Wei
Jailbreaking methods, which induce Multi-modal Large Language Models (MLLMs) to output harmful responses, raise significant safety concerns. Among these methods, gradient-based app…
Are Classification Robustness and Explanation Robustness Really Strongly Correlated? An Analysis Through Input Loss Landscape
Tiejin Chen, Wenwang Huang, Linsey Pang +2
This paper delves into the critical area of deep learning robustness, challenging the conventional belief that classification robustness and explanation robustness in image classif…
Uncertainty Regularized Evidential Regression
Kai Ye, Tiejin Chen, Hua Wei +1
The Evidential Regression Network (ERN) represents a novel approach that integrates deep learning with Dempster-Shafer's theory to predict a target and quantify the associated unce…