Teaching Models new APIs: Domain-Agnostic Simulators for Task Oriented Dialogue

Anonymous

Abstract: We demonstrate that large language models are able to simulate Task Oriented Dialogues in novel domains, provided only with an API implementation and a list of goals. We show these simulations can formulate online, automatic metrics that correlate well with human evaluations. Furthermore, by filtering for dialogues where goals are met, we can use simulation to repeatedly generate training data and improve the quality of the dialogues themselves. With no human intervention or domain-specific training data, our simulations bootstrap end-to-end models which achieve a 37\% error reduction over baseline in previously unseen domains. By including as few as 32 domain-specific conversations, bootstrapped models can match the performance of a fully-supervised model with $10\times$ more data.