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arXiv · 2309.12940

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
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Abstract

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.

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Haoyu Gao, Ting-En Lin, Hangyu Li, Min Yang, Yuchuan Wu, Wentao Ma, Yongbin Li. 2023-09-22. Self-Explanation Prompting Improves Dialogue Understanding in Large Language Models. https://arxiv.org/abs/2309.12940

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