@inproceedings{qin-goldwasser-2026-iterative,
    title = "Iterative Dual-Model Alignment for Story Evaluation",
    author = "Qin, Bruce  and
      Goldwasser, Dan",
    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.648/",
    pages = "14251--14264",
    ISBN = "979-8-89176-390-6",
    abstract = "Large language models (LLMs) can both evaluate and explain text quality; however, most existing evaluators operate as static classifiers and lack the ability to refine their reasoning through interaction. We propose an \textbf{Iterative Alpha{--}Beta Learning} framework that jointly trains two complementary 8B models: an Alpha ($\alpha$) classifier that assesses pairwise story engagement, and a Beta ($\beta$) generator that produces structured, rubric-guided comparative explanations. The two models co-evolve within a closed feedback loop: $\alpha$ provides probabilistic preference signals to guide $\beta${'}s Direct Preference Optimization (DPO), while $\beta${'}s improved explanations are reintegrated to retrain $\alpha$ via a KL-based contrastive objective. This dual optimization enables mutual learning: $\alpha$ gains interpretability and robustness from $\beta${'}s textual rationales, while $\beta$ acquires stronger alignment and discriminative precision from $\alpha${'}s confidence deltas. Experiments on human-annotated story-pair datasets HANNA show that the proposed system consistently outperforms strong single-model baselines in both accuracy and explanation quality across multiple iterative rounds."
}