The Power of the Noisy Channel: Unsupervised End-to-End Task-Oriented Dialogue with LLMs
<|reference_start|>The Power of the Noisy Channel: Unsupervised End-to-End Task-Oriented Dialogue with LLMs: Training task-oriented dialogue systems typically requires turn-level annotations for interacting with their APIs: e.g. a dialogue state and the system actions taken at each step. These annotations can be costly to produce, error-prone, and require both domain and annotation expertise. With advances in LLMs, we hypothesize unlabelled data and a schema definition are sufficient for building a working task-oriented dialogue system, completely unsupervised. Using only (1) a well-defined API schema (2) a set of unlabelled dialogues between a user and agent, we develop a novel approach for inferring turn-level annotations as latent variables using a noisy channel model. We iteratively improve these pseudo-labels with expectation-maximization (EM), and use the inferred labels to train an end-to-end dialogue agent. Evaluating our approach on the MultiWOZ benchmark, our method more than doubles the dialogue success rate of a strong GPT-3.5 baseline.<|reference_end|>
@article{king2024unsupervised,
    title={Unsupervised End-to-End Task-Oriented Dialogue with LLMs: The Power of
  the Noisy Channel},
    author={Brendan King and Jeffrey Flanigan},
    journal={arXiv preprint arXiv:2404.15219},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.15219},
    primaryClass={cs.CL}
}