InstructTODS: Large Language Models for End-to-End Task-Oriented
  Dialogue Systems
Willy Chung Samuel Cahyawijaya Bryan Wilie Holy Lovenia Pascale Fung
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/