This paper describes our system submitted to Dialogue Robot Competition 2022.
Our proposed system is a combined model of rule-based and generation-based
dialog systems. The system utilizes HyperCLOVA, a Japanese foundation model,
not only to generate responses but also summarization, search information, etc.
We also used our original speech recognition system, which was fine-tuned for
this dialog task. As a result, our system ranked second in the preliminary
round and moved on to the finals.

本文介绍了我们在 2022 年对话机器人竞赛中提交的系统。该系统是基于规则和生成式对话系统的组合模型。我们利用 HyperCLOVA（一种日本基础模型）来生成回复和摘要、搜索信息等。此外，我们还使用了我们原创的语音识别系统，并对其进行了本次对话任务的微调。结果，我们的系统在初赛中排名第二，晋级决赛。

基于 HyperCLOVA 的旅游指导机器人

Tourist Guidance Robot Based on HyperCLOVA

Many recent studies on large-scale language models have reported successful
in-context zero- and few-shot learning ability. However, the in-depth analysis
of when in-context learning occurs is still lacking. For example, it is unknown
how in-context learning performance changes as the training corpus varies.
Here, we investigate the effects of the source and size of the pretraining
corpus on in-context learning in HyperCLOVA, a Korean-centric GPT-3 model. From
our in-depth investigation, we introduce the following observations: (1)
in-context learning performance heavily depends on the corpus domain source,
and the size of the pretraining corpus does not necessarily determine the
emergence of in-context learning, (2) in-context learning ability can emerge
when a language model is trained on a combination of multiple corpora, even
when each corpus does not result in in-context learning on its own, (3)
pretraining with a corpus related to a downstream task does not always
guarantee the competitive in-context learning performance of the downstream
task, especially in the few-shot setting, and (4) the relationship between
language modeling (measured in perplexity) and in-context learning does not
always correlate: e.g., low perplexity does not always imply high in-context
few-shot learning performance.

研究了韩国中心型 GPT-3 模型 HyperCLOVA 中的上下文零样本和少样本学习，发现性能主要取决于语料库域源和预训练语料库的大小，可以通过组合多个语料库预先训练获得上下文学习能力.