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arXiv · 2406.09702

Detecting Response Generation Not Requiring Factual Judgment

Ryohei Kamei·Daiki Shiono·Reina Akama·Jun Suzuki
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Abstract

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.

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Ryohei Kamei, Daiki Shiono, Reina Akama, Jun Suzuki. 2024-06-14. Detecting Response Generation Not Requiring Factual Judgment. https://arxiv.org/abs/2406.09702

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