RABIT: Rationale-Based Distillation Towards Interpretable Automatic Speaking Assessment via a Small Language Model
Year: 2026
Type: conference-paper
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.
Institution: National Taiwan Normal University
Language: English
Subfield: Language and Linguistics
Field: Arts and Humanities
Domain: Social Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W7166859787