  Language models have steadily increased in size over the past few years. They
achieve a high level of performance on various natural language processing
(NLP) tasks such as question answering and summarization. Large language models
(LLMs) have been used for generation and can now output human-like text. Due to
this, there are other downstream tasks in the realm of dialog that can now
harness the LLMs' language understanding capabilities. Dialog evaluation is one
task that this paper will explore. It concentrates on prompting with LLMs:
BLOOM, OPT, GPT-3, Flan-T5, InstructDial and TNLGv2. The paper shows that the
choice of datasets used for training a model contributes to how well it
performs on a task as well as on how the prompt should be structured.
Specifically, the more diverse and relevant the group of datasets that a model
is trained on, the better dialog evaluation performs. This paper also
investigates how the number of examples in the prompt and the type of example
selection used affect the model's performance.
