2 citations · 5 across the 13 of their papers we have counts for
5 papers · 1 filter
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…
Athena 2.0: Discourse and User Modeling in Open Domain Dialogue
Omkar Patil, Lena Reed, Kevin K. Bowden +12
Conversational agents are consistently growing in popularity and many people interact with them every day. While many conversational agents act as personal assistants, they can hav…
Multimodal Learning Without Labeled Multimodal Data: Guarantees and Applications
Paul Pu Liang, Chun Kai Ling, Yun Cheng +6
In many machine learning systems that jointly learn from multiple modalities, a core research question is to understand the nature of multimodal interactions: how modalities combin…
Semantic Composition in Visually Grounded Language Models
Rohan Pandey
What is sentence meaning and its ideal representation? Much of the expressive power of human language derives from semantic composition, the mind's ability to represent meaning hie…
Syntax-guided Neural Module Distillation to Probe Compositionality in Sentence Embeddings
Rohan Pandey
Past work probing compositionality in sentence embedding models faces issues determining the causal impact of implicit syntax representations. Given a sentence, we construct a neur…