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.
@article{arxiv.2406.09702,
title = {Detecting Response Generation Not Requiring Factual Judgment},
author = {Ryohei Kamei and Daiki Shiono and Reina Akama and Jun Suzuki},
journal= {arXiv preprint arXiv:2406.09702},
year = {2024}
}