@inproceedings{li-etal-2025-two,
    title = "Two Heads Are Better Than One: Dual-Model Verbal Reflection at Inference-Time",
    author = "Li, Jiazheng  and
      Zhou, Yuxiang  and
      Lu, Junru  and
      Tyen, Gladys  and
      Gui, Lin  and
      Aloisi, Cesare  and
      He, Yulan",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.155/",
    pages = "3119--3140",
    ISBN = "979-8-89176-332-6",
    abstract = "Although preference optimization methods have improved reasoning performance in Large Language Models (LLMs), they often lack transparency regarding why one reasoning outcome is preferred over another. This limitation is especially critical in Automated Student Answer Scoring (ASAS), where explainability is essential to justify assessment outcomes. Verbal reinforcement learning offers the potential to generate explicit reflection, but it tends to produce superficial critiques that can harm assessment performance. Existing LLMs also struggle to reliably detect subtle reasoning errors in ASAS tasks. Moreover, manually identifying intermediate reasoning errors is expensive and difficult to scale. To address these challenges, we introduce a **contrastive reflection synthesis pipeline** that generates precise verbal feedback by identifying discrepancies in structure reasoning graph paths. Leveraging these synthetic reflection data, we propose *DARS*, a Dual-model Reflective Scoring framework featuring a dedicated Critic model trained for effective reflection. *DARS* achieves strong performance and consistently outperforms existing ASAS baselines across all evaluation metrics. Extensive experiments further provide novel insights into the value of reflection data, framework design, and the scaling behavior of *DARS*. We release the DARS code at https://github.com/lijiazheng99/DARS."
}