2024/06/14 by Ryohei Kamei, Daiki Shiono, Kamei, Ryohei +5
Computer Science · Psychology · #Adversarial Robustness in Machine Learning #Computation and Language (cs.CL) #Deception detection and forensic psychology #FOS: Computer and information sciences #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2406.09702
openalex publication_date 2024/06/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.