DivTOD: Unleashing the Power of LLMs for Diversifying Task-Oriented Dialogue Representations

• URL: http://arxiv.org/abs/2404.00557v1
• Date: Sun, 31 Mar 2024 04:36:57 GMT
• Title: DivTOD: Unleashing the Power of LLMs for Diversifying Task-Oriented Dialogue Representations
• Authors: Weihao Zeng, Dayuan Fu, Keqing He, Yejie Wang, Yukai Xu, Weiran Xu,
• Abstract summary: 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. We propose a novel dialogue pre-training model called DivTOD, which collaborates with LLMs to learn diverse task-oriented dialogue representations.
• Score: 21.814490079113323
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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