6 papers
Beyond Semantic Equivalence: Logical Graphs for LLM Uncertainty Quantification
Yanni Dong, Minghua Liu, Meiling Zhu +3
Large Language Models often produce confidently stated yet unreliable outputs, posing critical challenges for deployment in safety-sensitive applications. Existing uncertainty metr…
Quantum states supported by matroids
Xiaowei Huang, Fei Shi, Lijun Zhang +1
In this work, we establish a structural correspondence between quantum states and matroid theory. This connection demonstrates that key properties of quantum states, including enta…
Shapley Uncertainty in Natural Language Generation
Meilin Zhu, Gaojie Jin, Xiaowei Huang +1
In question-answering tasks, determining when to trust the outputs is crucial to the alignment of large language models (LLMs). Kuhn et al. (2023) introduces semantic entropy as a…
Preference Alignment on Diffusion Model: A Comprehensive Survey for Image Generation and Editing
Sihao Wu, Xiaonan Si, Chi Xing +5
The integration of preference alignment with diffusion models (DMs) has emerged as a transformative approach to enhance image generation and editing capabilities. Although integrat…
Invariant Correlation of Representation with Label: Enhancing Domain Generalization in Noisy Environments
Gaojie Jin, Ronghui Mu, Xinping Yi +2
The Invariant Risk Minimization (IRM) approach aims to address the challenge of domain generalization by training a feature representation that remains invariant across multiple en…
Training Verification-Friendly Neural Networks via Neuron Behavior Consistency
Zongxin Liu, Zhe Zhao, Fu Song +4
Formal verification provides critical security assurances for neural networks, yet its practical application suffers from the long verification time. This work introduces a novel m…