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

Moral framing and sentiment can affect a variety of online and offline behaviors, including donation, environmental action, political engagement, and protest. Various computational methods in Natural Language Processing (NLP) have been used to detect moral sentiment from textual data, but achieving strong performance in such subjective tasks requires large, hand-annotated datasets. Previous corpora annotated for moral sentiment have proven valuable, and have generated new insights both within NLP and across the social sciences, but have been limited to Twitter. To facilitate improving our understanding of the role of moral rhetoric, we present the Moral Foundations Reddit Corpus, a collection of 16,123 English Reddit comments that have been curated from 12 distinct subreddits, hand-annotated by at least three trained annotators for 8 categories of moral sentiment (i.e., Care, Proportionality, Equality, Purity, Authority, Loyalty, Thin Morality, Implicit/Explicit Morality) based on the updated Moral Foundations Theory (MFT) framework. We evaluate baselines using large language models (Llama3-8B, Ministral-8B) in zero-shot, few-shot, and PEFT (Parameter-Efficient Fine-Tuning) settings, comparing their performance to fine-tuned encoder-only models like BERT (Bidirectional Encoder Representations from Transformers). The results show that LLMs continue to lag behind fine-tuned encoders on this subjective task, underscoring the ongoing need for human-annotated moral corpora for AI alignment evaluation. Keywords: moral sentiment annotation, moral values, moral foundations theory, multi-label text classification, large language models, benchmark dataset, evaluation and alignment resource