Task-oriented dialogue (TOD) systems facilitate users in executing various activities via multi-turn dialogues, but Large Language Models (LLMs) often struggle to comprehend these intricate contexts. In this study, we propose a novel "Self-Explanation" prompting strategy to enhance the comprehension abilities of LLMs in multi-turn dialogues. This task-agnostic approach requires the model to analyze each dialogue utterance before task execution, thereby improving performance across various dialogue-centric tasks. Experimental results from six benchmark datasets confirm that our method consistently outperforms other zero-shot prompts and matches or exceeds the efficacy of few-shot prompts, demonstrating its potential as a powerful tool in enhancing LLMs' comprehension in complex dialogue tasks.
@article{arxiv.2309.12940,
title = {Self-Explanation Prompting Improves Dialogue Understanding in Large Language Models},
author = {Haoyu Gao and Ting-En Lin and Hangyu Li and Min Yang and Yuchuan Wu and Wentao Ma and Yongbin Li},
journal= {arXiv preprint arXiv:2309.12940},
year = {2023}
}