InstructTODS: Large Language Models for End-to-End Task-Oriented Dialogue Systems

• URL: http://arxiv.org/abs/2310.08885v1
• Date: Fri, 13 Oct 2023 06:36:26 GMT
• Title: InstructTODS: Large Language Models for End-to-End Task-Oriented Dialogue Systems
• Authors: Willy Chung, Samuel Cahyawijaya, Bryan Wilie, Holy Lovenia, Pascale Fung
• Abstract summary: Large language models (LLMs) have been used for diverse tasks in natural language processing (NLP) We present InstructTODS, a novel framework for zero-shot end-to-end task-oriented dialogue systems. InstructTODS generates a proxy belief state that seamlessly translates user intentions into dynamic queries.
• Score: 60.53276524369498
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Large language models (LLMs) have been used for diverse tasks in natural language processing (NLP), yet remain under-explored for task-oriented dialogue systems (TODS), especially for end-to-end TODS. We present InstructTODS, a novel off-the-shelf framework for zero-shot end-to-end task-oriented dialogue systems that can adapt to diverse domains without fine-tuning. By leveraging LLMs, InstructTODS generates a proxy belief state that seamlessly translates user intentions into dynamic queries for efficient interaction with any KB. Our extensive experiments demonstrate that InstructTODS achieves comparable performance to fully fine-tuned TODS in guiding dialogues to successful completion without prior knowledge or task-specific data. Furthermore, a rigorous human evaluation of end-to-end TODS shows that InstructTODS produces dialogue responses that notably outperform both the gold responses and the state-of-the-art TODS in terms of helpfulness, informativeness, and humanness. Moreover, the effectiveness of LLMs in TODS is further supported by our comprehensive evaluations on TODS subtasks: dialogue state tracking, intent classification, and response generation. Code and implementations could be found here https://github.com/WillyHC22/InstructTODS/

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