Zander Brumbaugh — accepted AI-conference papers

2024

I Can Tell What I am Doing: Toward Real-World Natural Language Grounding of Robot Experiences

Zihan Wang, Brian Liang, Varad Dhat, Zander Brumbaugh, Nick Walker, Ranjay Krishna +1

CoRL 2024poster

Understanding robot behaviors and experiences through natural language is crucial for developing intelligent and transparent robotic systems. Recent advancement in large language models (LLMs) makes it possible to translate complex, multi-modal robotic experiences into coherent, human-readable narra…

Cited by 4 Source ↗Scholar ↗

2024

Set the Clock: Temporal Alignment of Pretrained Language Models

Bowen Zhao, Zander Brumbaugh, Yizhong Wang, Hannaneh Hajishirzi, Noah Smith

ACL 2024findings

Language models (LMs) are trained on web text originating from many points in time and, in general, without any explicit temporal grounding. This work investigates the temporal chaos of pretrained LMs and explores various methods to align their internal knowledge to a target time, which we call “tem…

Cited by 15 Source ↗PDF ↗code ↗Scholar ↗