5 citations · 7 across the 5 of their papers we have counts for
5 papers
Self-Correcting Decoding with Generative Feedback for Mitigating Hallucinations in Large Vision-Language Models
Ce Zhang, Zifu Wan, Zhehan Kan +7
While recent Large Vision-Language Models (LVLMs) have shown remarkable performance in multi-modal tasks, they are prone to generating hallucinatory text responses that do not alig…
Evaluating Deep Unlearning in Large Language Models
Ruihan Wu, Chhavi Yadav, Russ Salakhutdinov +1
Machine unlearning has emerged as an important component in developing safe and trustworthy models. Prior work on fact unlearning in LLMs has mostly focused on removing a specified…
IoT-LM: Large Multisensory Language Models for the Internet of Things
Shentong Mo, Russ Salakhutdinov, Louis-Philippe Morency +1
The Internet of Things (IoT) network integrating billions of smart physical devices embedded with sensors, software, and communication technologies is a critical and rapidly expand…
MMoE: Enhancing Multimodal Models with Mixtures of Multimodal Interaction Experts
Haofei Yu, Zhengyang Qi, Lawrence Jang +3
Advances in multimodal models have greatly improved how interactions relevant to various tasks are modeled. Today's multimodal models mainly focus on the correspondence between ima…
MultiIoT: Benchmarking Machine Learning for the Internet of Things
Shentong Mo, Louis-Philippe Morency, Russ Salakhutdinov +1
The next generation of machine learning systems must be adept at perceiving and interacting with the physical world through a diverse array of sensory channels. Commonly referred t…