  With the remarkable development of large language models (LLMs), ensuring the
factuality of output has become a challenge. However, having all the contents
of the response with given knowledge or facts is not necessarily a good thing
in dialogues. This study aimed to achieve both attractiveness and factuality in
a dialogue response for which a task was set to predict sentences that do not
require factual correctness judgment such as agreeing, or personal
opinions/feelings. We created a dataset, dialogue dataset annotated with
fact-check-needed label (DDFC), for this task via crowdsourcing, and
classification tasks were performed on several models using this dataset. The
model with the highest classification accuracy could yield about 88% accurate
classification results.
