Preni Golazizian, Elnaz Rahmati, Jackson Trager, Zhivar Sourati, Nona Ghazizadeh, Georgios Chochlakis, Jose J. Alcocer, Kerby Bennett, Aarya Vijay Devnani, Parsa Hejabi, Harry G. Muttram, Akshay Kiran Padte, Mehrshad Saadatinia, Chenhao Wu, Alireza Salkhordeh Ziabari, Michael Sierra-Arévalo, Nicholas Weller, Shrikanth Narayanan, Benjamin A.t. Graham, Morteza Dehghani. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

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, majorityvoting 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 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. 1

Alireza Salkhordeh Ziabari, Ali Omrani, Parsa Hejabi, Preni Golazizian, Brendan Kennedy, Payam Piray, Morteza Dehghani. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.