Listen, Correct, and Feed Back: Spoken Pedagogical Feedback Generation

arXiv:2604.14177v1 Announce Type: new
Abstract: Grammatical error correction (GEC) and explanation (GEE) have made rapid progress, but real teaching scenarios also require emph{learner-friendly pedagogical feedback} that is actionable, level-appropriate, and encouraging. We introduce textbf{SPFG} (textbf{S}poken textbf{P}edagogical textbf{F}eedback textbf{G}eneration), a dataset built based on the Speak & Improve Challenge 2025 corpus, pairing fluency-oriented transcriptions with GEC targets and emph{human-verified} teacher-style feedback, including preferred/rejected feedback pairs for preference learning. We study a transcript-based Spoken Grammatical Error Correction (SGEC) setting and evaluate three instruction-tuned LLMs (Qwen2.5, Llama-3.1, and GLM-4), comparing supervised fine-tuning (SFT) with preference-based alignment (using DPO and KTO) for jointly generating corrections and feedback. Results show that SFT provides the most consistent improvements, while DPO/KTO yield smaller or mixed gains, and that correction quality and feedback quality are weakly coupled. Our implementation is available at https://github.com/Skywalker-Harrison/spfg.