  Large language models (LLMs) are supposed to acquire unconscious human
knowledge and feelings, such as social common sense and biases, by training
models from large amounts of text. However, it is not clear how much the
sentiments of specific social groups can be captured in various LLMs. In this
study, we focus on social groups defined in terms of nationality, religion, and
race/ethnicity, and validate the extent to which sentiments between social
groups can be captured in and extracted from LLMs. Specifically, we input
questions regarding sentiments from one group to another into LLMs, apply
sentiment analysis to the responses, and compare the results with social
surveys. The validation results using five representative LLMs showed higher
correlations with relatively small p-values for nationalities and religions,
whose number of data points were relatively large. This result indicates that
the LLM responses including the inter-group sentiments align well with actual
social survey results.
