Towards a Zero-Data, Controllable, Adaptive Dialog System
Dirk Väth Lindsey Vanderlyn Ngoc Thang Vu
Conversational Tree Search (V\"ath et al., 2023) is a recent approach to
controllable dialog systems, where domain experts shape the behavior of a
Reinforcement Learning agent through a dialog tree. The agent learns to
efficiently navigate this tree, while adapting to information needs, e.g.,
domain familiarity, of different users. However, the need for additional
training data hinders deployment in new domains. To address this, we explore
approaches to generate this data directly from dialog trees. We improve the
original approach, and show that agents trained on synthetic data can achieve
comparable dialog success to models trained on human data, both when using a
commercial Large Language Model for generation, or when using a smaller
open-source model, running on a single GPU. We further demonstrate the
scalability of our approach by collecting and testing on two new datasets:
ONBOARD, a new domain helping foreign residents moving to a new city, and the
medical domain DIAGNOSE, a subset of Wikipedia articles related to scalp and
head symptoms. Finally, we perform human testing, where no statistically
significant differences were found in either objective or subjective measures
between models trained on human and generated data.