@inproceedings{yan-etal-2026-rabit,
    title = "{RABIT}: Rationale-Based Distillation Towards Interpretable Automatic Speaking Assessment via a Small Language Model",
    author = "Yan, Bi-Cheng  and
      Lin, Hong-Yun  and
      Chao, Fu-An  and
      Li, Jiun-Ting  and
      Chen, Berlin",
    editor = "Kochmar, Ekaterina  and
      Alhafni, Bashar  and
      Bann{\`o}, Stefano  and
      Bexte, Marie  and
      Burstein, Jill  and
      Horbach, Andrea  and
      Laarmann-Quante, Ronja  and
      Tack, Anais  and
      Yaneva, Victoria  and
      Yuan, Zheng",
    booktitle = "Proceedings of the 21st Workshop on Innovative Use of {NLP} for Building Educational Applications ({BEA} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.bea-1.9/",
    pages = "108--117",
    ISBN = "979-8-89176-409-5",
    abstract = "Automatic speaking assessment (ASA) manages to quantify the language competence of foreign language learners by providing a proficiency score based on their spoken response. Existing efforts in ASA typically employ a neural grader integrated with a set of handcrafted features to assess learners' oral proficiency from multiple facets. Despite decent performance, the black-box nature of these neural graders remains a significant barrier to providing interpretable explanations for the grading results. In light of this, we propose RABIT for ASA, a novel Rationale-based knowledge distillation framework for interpretable grading decisions via a small language model. Specifically, RABIT first extracts multi-faceted grading rationales from a large language model (LLM) pertaining to the learner{'}s response and the scoring guidelines. Subsequently, a compact yet efficient language model, equipped with distinct output heads, is jointly optimized to estimate a proficiency score while generating a sequence of grading rationales in an autoregressive manner. A series of experiments conducted on General English Proficiency Test (GEPT) dataset validates the feasibility and superiority of our method over several cutting-edge baselines."
}