  Task-oriented dialogue systems use four connected modules, namely, Natural
Language Understanding (NLU), a Dialogue State Tracking (DST), Dialogue Policy
(DP) and Natural Language Generation (NLG). A research challenge is to learn
each module with the least amount of samples (i.e., few-shots) given the high
cost related to the data collection. The most common and effective technique to
solve this problem is transfer learning, where large language models, either
pre-trained on text or task-specific data, are fine-tuned on the few samples.
These methods require fine-tuning steps and a set of parameters for each task.
Differently, language models, such as GPT-2 (Radford et al., 2019) and GPT-3
(Brown et al., 2020), allow few-shot learning by priming the model with few
examples. In this paper, we evaluate the priming few-shot ability of language
models in the NLU, DST, DP and NLG tasks. Importantly, we highlight the current
limitations of this approach, and we discuss the possible implication for
future work.
