LAD: Language Models as Data for Zero-Shot Dialog

However, fine-tuning can be impractical dialog remains elusive. A likely reason for this (e.g., in academic settings) with large LMs (e.g., discrepancy is that dialog models require significant GPT-3) due to the cost, computational power and data because they need to learn task-specific immutable architectures. To this end, this paper structural constraints, such as the domain ontology aims to address the following: 'How can we leverage and the dialog policy. While large language the strong language understanding and generation models (e.g., GPT-3) exhibit strong language understanding abilities of large LMs to facilitate zero-shot and generation abilities (Brown et al., generalization in task-oriented dialog?' 2020), they have no a priori knowledge of the Given the in-context meta-learning abilities of structural constraints implied by a specific (unseen) large LMs (Brown et al., 2020), prior work has problem setting (e.g., relevant intents, dialog policy, explored prompt-engineering or prompt-tuning etc.). As such, in order to adapt a pre-trained (Reynolds and McDonell, 2021; Lester et al., 2021; LM for task-oriented dialog, it is necessary to impose Madotto et al., 2021). Well-designed prompts can structural constraints on the unstructured convey the necessary structural constraints.