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.

通过自解释提示策略来增强大语言模型在多轮对话中的理解能力，实验证实该方法在多个基准数据集上持续优于其他零样本提示，并达到或超过少样本提示的效果，证明其在提升大语言模型在复杂对话任务中的理解能力方面具有潜力。