  This paper explores SynTOD, a new synthetic data generation approach for
developing end-to-end Task-Oriented Dialogue (TOD) Systems capable of handling
complex tasks such as intent classification, slot filling, conversational
question-answering, and retrieval-augmented response generation, without
relying on crowdsourcing or real-world data. SynTOD utilizes a state transition
graph to define the desired behavior of a TOD system and generates diverse,
structured conversations through random walks and response simulation using
large language models (LLMs). In our experiments, using graph-guided response
simulations leads to significant improvements in intent classification, slot
filling and response relevance compared to naive single-prompt simulated
conversations. We also investigate the end-to-end TOD effectiveness of
different base and instruction-tuned LLMs, with and without the constructed
synthetic conversations. Finally, we explore how various LLMs can evaluate
responses in a TOD system and how well they are correlated with human
judgments. Our findings pave the path towards quick development and evaluation
of domain-specific TOD systems. We release our datasets, models, and code for
research purposes.
