4 papers · 1 filter
Aligning Dialogue Agents with Global Feedback via Large Language Model Multimodal Reward Decomposition
Dong Won Lee, Hae Won Park, Cynthia Breazeal +1
We propose a large language model based reward decomposition framework for aligning dialogue agents using only a single session-level feedback signal. We leverage the reasoning cap…
Leveraging Large Language Models to Identify Conversation Threads in Collaborative Learning
Prerna Ravi, Dong Won Lee, Beatriz Flamia +5
Understanding how ideas develop and flow in small-group conversations is critical for analyzing collaborative learning. A key structural feature of these interactions is threading,…
Does "Reasoning" with Large Language Models Improve Recognizing, Generating, and Reframing Unhelpful Thoughts?
Yilin Qi, Dong Won Lee, Cynthia Breazeal +1
Cognitive Reframing, a core element of Cognitive Behavioral Therapy (CBT), helps individuals reinterpret negative experiences by finding positive meaning. Recent advances in Large…
Improving Dialogue Agents by Decomposing One Global Explicit Annotation with Local Implicit Multimodal Feedback
Dong Won Lee, Hae Won Park, Yoon Kim +2
We describe an approach for aligning an LLM-based dialogue agent based on global (i.e., dialogue-level) rewards, while also taking into account naturally-occurring multimodal signa…