3 citations · 10 across the 10 of their papers we have counts for
10 papers
MultiMed: Massively Multimodal and Multitask Medical Understanding
Shentong Mo, Paul Pu Liang
Biomedical data is inherently multimodal, consisting of electronic health records, medical imaging, digital pathology, genome sequencing, wearable sensors, and more. The applicatio…
Foundations of Multisensory Artificial Intelligence
Paul Pu Liang
Building multisensory AI systems that learn from multiple sensory inputs such as text, speech, video, real-world sensors, wearable devices, and medical data holds great promise for…
Semantically Corrected Amharic Automatic Speech Recognition
Samuael Adnew, Paul Pu Liang
Automatic Speech Recognition (ASR) can play a crucial role in enhancing the accessibility of spoken languages worldwide. In this paper, we build a set of ASR tools for Amharic, a l…
Think Twice: Perspective-Taking Improves Large Language Models' Theory-of-Mind Capabilities
Alex Wilf, Sihyun Shawn Lee, Paul Pu Liang +1
Human interactions are deeply rooted in the interplay of thoughts, beliefs, and desires made possible by Theory of Mind (ToM): our cognitive ability to understand the mental states…
Comparative Knowledge Distillation
Alex Wilf, Alex Tianyi Xu, Paul Pu Liang +3
In the era of large scale pretrained models, Knowledge Distillation (KD) serves an important role in transferring the wisdom of computationally heavy teacher models to lightweight,…
Towards Vision-Language Mechanistic Interpretability: A Causal Tracing Tool for BLIP
Vedant Palit, Rohan Pandey, Aryaman Arora +1
Mechanistic interpretability seeks to understand the neural mechanisms that enable specific behaviors in Large Language Models (LLMs) by leveraging causality-based methods. While t…