@inproceedings{gao-etal-2024-self,
    title = "Self-Explanation Prompting Improves Dialogue Understanding in Large Language Models",
    author = "Gao, Haoyu  and
      Lin, Ting-En  and
      Li, Hangyu  and
      Yang, Min  and
      Wu, Yuchuan  and
      Ma, Wentao  and
      Huang, Fei  and
      Li, Yongbin",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1269",
    pages = "14567--14578",
    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.",
}

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