DivTOD: Unleashing the Power of LLMs for Diversifying Task-Oriented Dialogue Representations
<|reference_start|>DivTOD: Unleashing the Power of LLMs for Diversifying Task-Oriented Dialogue Representations: Language models pre-trained on general text have achieved impressive results in diverse fields. Yet, the distinct linguistic characteristics of task-oriented dialogues (TOD) compared to general text limit the practical utility of existing language models. Current task-oriented dialogue pre-training methods overlook the one-to-many property of conversations, where multiple responses can be appropriate given the same conversation context. In this paper, we propose a novel dialogue pre-training model called DivTOD, which collaborates with LLMs to learn diverse task-oriented dialogue representations. DivTOD guides LLMs in transferring diverse knowledge to smaller models while removing domain knowledge that contradicts task-oriented dialogues. Experiments show that our model outperforms strong TOD baselines on various downstream dialogue tasks and learns the intrinsic diversity of task-oriented dialogues.<|reference_end|>
@article{zeng2024divtod:,
    title={DivTOD: Unleashing the Power of LLMs for Diversifying Task-Oriented
  Dialogue Representations},
    author={Weihao Zeng, Dayuan Fu, Keqing He, Yejie Wang, Yukai Xu, Weiran Xu},
    journal={arXiv preprint arXiv:2404.00557},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.00557},
    primaryClass={cs.CL}
}