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.

翻译：任务导向型对话系统通过多轮对话协助用户完成各类活动，但大语言模型常难以理解这些复杂语境。本研究提出一种创新的"自解释"提示策略，以增强大语言模型在多轮对话中的理解能力。该任务无关方法要求模型在执行任务前分析每个对话语句，从而提升各类对话核心任务的性能。六个基准数据集的实验结果证实，该方法始终优于其他零样本提示，其效果达到或超越少样本提示，彰显其作为增强大语言模型理解复杂对话任务的有力工具的潜力。