Detecting Response Generation Not Requiring Factual Judgment
<|reference_start|>Detecting Response Generation Not Requiring Factual Judgment: 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.<|reference_end|>
@article{kamei2024detecting,
    title={Detecting Response Generation Not Requiring Factual Judgment},
    author={Ryohei Kamei, Daiki Shiono, Reina Akama, Jun Suzuki},
    journal={arXiv preprint arXiv:2406.09702},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.09702},
    primaryClass={cs.CL}
}