4 papers
Vision-and-Language Training Helps Deploy Taxonomic Knowledge but Does Not Fundamentally Alter It
Yulu Qin, Dheeraj Varghese, Adam Dahlgren Lindström +3
Does vision-and-language (VL) training change the linguistic representations of language models in meaningful ways? Most results in the literature have shown inconsistent or margin…
Trustworthy AI Psychotherapy: Multi-Agent LLM Workflow for Counseling and Explainable Mental Disorder Diagnosis
Mithat Can Ozgun, Jiahuan Pei, Koen Hindriks +3
LLM-based agents have emerged as transformative tools capable of executing complex tasks through iterative planning and action, achieving significant advancements in understanding…
SCOUT: A Situated and Multi-Modal Human-Robot Dialogue Corpus
Stephanie M. Lukin, Claire Bonial, Matthew Marge +14
We introduce the Situated Corpus Of Understanding Transactions (SCOUT), a multi-modal collection of human-robot dialogue in the task domain of collaborative exploration. The corpus…
Human-Robot Dialogue Annotation for Multi-Modal Common Ground
Claire Bonial, Stephanie M. Lukin, Mitchell Abrams +11
In this paper, we describe the development of symbolic representations annotated on human-robot dialogue data to make dimensions of meaning accessible to autonomous systems partici…