Bootstrap Wayfinding Questions to Elicit Emotion Shift Reasoning with Large Language Models

Vy Nguyen, Xiuzhen Zhang, Feng Xia

Abstract: Emotions are integral to communication, and understanding emotions is critically important for healthcare applications such as mental well-being and empathetic support. However, in conversational exchanges, emotions constantly shift as conversations unfold. Existing studies on reasoning about emotion shift triggers (ESR) require extensive manual annotation of both utterance-level emotion labels and emotion shift triggers. In this research, we propose a novel approach, the Wayfinding Instruction Tuning (WIT) framework, to tackle the ESR challenge in a more efficient manner with minimal dependency on emotion labels. WIT employs a large language model (LLM) to generate sequences of dynamic, context-sensitive wayfinding questions designed to guide the instruction tuning of another LLM on the ESR task. In contrast to conventional prompting, our key innovation is a structured \textit{wayfinding prompting strategy}, which systematically decomposes the ESR process into carefully designed sub-questions. Together with their ground-truth-aligned answers, these sub-questions guide the model to learn how to detect changes in emotion and identify their triggers. Our experiments show that, even when fine-tuned on a medium-sized dataset of 100 training instances, using only pre- and post-shift emotion labels, WIT can directly predict emotion shifts without first identifying the emotion of each utterance. This design avoids intermediate emotion classification and its associated error propagation, a common issue in two-step pipelines. WIT achieves comparable performance (F1: 0.749 vs 0.760) to state-of-the-art supervised models while requiring only shift-boundary labels during training and zero annotation at inference, providing a practical alternative when complete utterance labels are unavailable.