@inproceedings{hejabi-etal-2026-flip,
    title = "Flip-Flop Consistency: Unsupervised Training for Robustness to Prompt Perturbations in {LLM}s",
    author = "Hejabi, Parsa  and
      Rahmati, Elnaz  and
      Ziabari, Alireza Salkhordeh  and
      Dehghani, Morteza",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.71/",
    pages = "1571--1587",
    ISBN = "979-8-89176-390-6",
    abstract = "Large Language Models (LLMs) often produce inconsistent answers when faced with different phrasings of the same prompt. In this paper, we propose Flip-Flop Consistency (F$^2$C), an unsupervised training method that improves robustness to such perturbations. F$^2$C is composed of two key components. The first, Consensus Cross-Entropy (CCE), uses a majority vote across prompt variations to create a hard pseudo-label. The second is a representation alignment loss that pulls lower-confidence and non-majority predictors toward the consensus established by high-confidence, majority-voting variations. We evaluate our method on 11 datasets spanning four NLP tasks, with 4{--}15 prompt variations per dataset. On average, F$^2$C raises observed agreement by 11.62{\%}, improves mean $F_1$ by 8.94{\%}, and reduces performance variance across formats by 3.29{\%}. In out-of-domain evaluations, F$^2$C generalizes effectively, increasing $\overline{F_1}$ and agreement while decreasing variance across most source-target pairs. Finally, when trained on only a subset of prompt perturbations and evaluated on held-out formats, F$^2$C consistently improves both performance and agreement while reducing variance. These findings highlight F$^2$C as an effective unsupervised method for enhancing LLM consistency, performance, and generalization under prompt perturbations."
}