3 citations · 10 across the 39 of their papers we have counts for
9 papers · 1 filter
UniGame: Turning a Unified Multimodal Model Into Its Own Adversary
Zhaolong Su, Wang Lu, Hao Chen +2
Unified Multimodal Models (UMMs) have shown impressive performance in both understanding and generation with a single architecture. However, UMMs still exhibit a fundamental incons…
Learning to Think Fast and Slow for Visual Language Models
Chenyu Lin, Cheng Chi, Jinlin Wu +2
When faced with complex problems, we tend to engage in slower, more deliberate thinking. In contrast, for simple questions we give quick, intuitive responses. This dual-system thin…
A Definition of AGI
Dan Hendrycks, Dawn Song, Christian Szegedy +30
The lack of a concrete definition for Artificial General Intelligence (AGI) obscures the gap between today's specialized AI and human-level cognition. This paper introduces a quant…
General Exploratory Bonus for Optimistic Exploration in RLHF
Wendi Li, Changdae Oh, Sharon Li
Optimistic exploration is central to improving sample efficiency in reinforcement learning with human feedback, yet existing exploratory bonus methods to incentivize exploration of…
KnowledgeSmith: Uncovering Knowledge Updating in LLMs with Model Editing and Unlearning
Yinyi Luo, Zhexian Zhou, Hao Chen +4
Knowledge editing and machine unlearning are two popular approaches for large language models (LLMs) to stay up-to-date. However, the knowledge updating mechanism of LLMs remains l…
How Well Can Preference Optimization Generalize Under Noisy Feedback?
Shawn Im, Sharon Li
As large language models (LLMs) advance their capabilities, aligning these models with human preferences has become crucial. Preference optimization, which trains models to disting…
Unsupervised Domain Adaptation for Binary Classification with an Unobservable Source Subpopulation
Chao Ying, Jun Jin, Haotian Zhang +4
We study an unsupervised domain adaptation problem where the source domain consists of subpopulations defined by the binary label and a binary background (or environment) .…
Harnessing Feature Resonance under Arbitrary Target Alignment for Out-of-Distribution Node Detection
Shenzhi Yang, Junbo Zhao, Sharon Li +4
Detecting out-of-distribution (OOD) nodes in the graph-based machine-learning field is challenging, particularly when in-distribution (ID) node multi-category labels are unavailabl…
Distributional Statistics Restore Training Data Auditability in One-step Distilled Diffusion Models
Muxing Li, Zesheng Ye, Sharon Li +3
The proliferation of diffusion models trained on web-scale, provenance-uncertain image collections has made it essential, yet technically unresolved, to determine whether a model h…