Investigating the Effects of Zero-Shot Chain-of-Thought on Empathetic Dialogue Generation

Young-Jun Lee, Dokyong Lee, Jihui Im, Joo Won Sung, Ho-Jin Choi

Keywords: Zero-shot Chain-of-Thought, InstructGPT, Empathetic Dialogue Generation

TL;DR: The study examines the effectiveness of the Zero-shot Chain-of-Thought (CoT) approach in enhancing the empathetic reasoning of Large Language Models (LLMs)

Abstract: This study investigates the effectiveness of the Zero-shot Chain-of-Thought (CoT) approach, specifically the "Let's think step by step.'', in boosting the empathetic reasoning capabilities of Large Language Models (LLMs). Our experiments, however, reveal that Zero-shot CoT does not sufficiently enhance the empathetic reasoning of LLMs as compared to Zero-shot In-Context Learning (ICL), according to a variety of performance metrics. Importantly, we discovered that the perspective-taking prompting method, or ``\textit{Let's put {speaker} into {interlocutor}'s shoes.}'', surpasses the performance of Zero-shot CoT, especially in terms of emotion and intent accuracy, with an improvement of 21\% and 7\% respectively. The source code will be released after publication.

Submission Number: 55