Self-Explanation Prompting Improves Dialogue Understanding in Large
  Language Models
Haoyu Gao Ting-En Lin Hangyu Li Min Yang Yuchuan Wu Wentao Ma Yongbin Li
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.