A Large Collection of Model-generated Contradictory Responses for Consistency-aware Dialogue Systems
<|reference_start|>A Large Collection of Model-generated Contradictory Responses for Consistency-aware Dialogue Systems: Mitigating the generation of contradictory responses poses a substantial challenge in dialogue response generation. The quality and quantity of available contradictory response data play a vital role in suppressing these contradictions, offering two significant benefits. First, having access to large contradiction data enables a comprehensive examination of their characteristics. Second, data-driven methods to mitigate contradictions may be enhanced with large-scale contradiction data for training. Nevertheless, no attempt has been made to build an extensive collection of model-generated contradictory responses. In this paper, we build a large dataset of response generation models' contradictions for the first time. Then, we acquire valuable insights into the characteristics of model-generated contradictions through an extensive analysis of the collected responses. Lastly, we also demonstrate how this dataset substantially enhances the performance of data-driven contradiction suppression methods.<|reference_end|>
@article{sato2024a,
    title={A Large Collection of Model-generated Contradictory Responses for
  Consistency-aware Dialogue Systems},
    author={Shiki Sato, Reina Akama, Jun Suzuki, Kentaro Inui},
    journal={arXiv preprint arXiv:2403.12500},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.12500},
    primaryClass={cs.CL}
}