Mechanism

Token-level negative-feedback objective that penalizes toxic, repetitive, or invalid tokens yt−y_t^- by contrasting their logit directly against a top-kk plausible positive token sampled from the model's own distribution at position tt, avoiding sequence-level unlikelihood collapse.

References

  1. Neural Text Generation with Unlikelihood Training ↗

    Sean Welleck, Ilia Kulikov, Stephen Roller, Emily Dinan, Kyunghyun Cho, Jason Weston · 2019

  2. The CRINGE Loss: Learning what language not to model ↗

    Leonard Adolphs, Tianyu Gao, Jing Xu, Kurt Shuster, Sainbayar Sukhbaatar, Jason Weston · 2022

  3. Some things are more CRINGE than others: Iterative Preference Optimization with the Pairwise Cringe Loss ↗

    Jing Xu, Andrew Lee, Sainbayar Sukhbaatar, Jason Weston · 2023