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

• URL: http://arxiv.org/abs/2604.14177v1
• Date: Sat, 28 Mar 2026 07:47:29 GMT
• Title: Listen, Correct, and Feed Back: Spoken Pedagogical Feedback Generation
• Abstract summary: We introduce textbfSPFG (textbfSpoken textbfPedagogical textbfFeedback textbfGeneration), a dataset built based on the Speak & Improve Challenge 2025 corpus.<n>We study a transcript-based Grammatical Error Correction (SGEC) setting and evaluate three instruction-tuned LLMs (Qwen2.5, Llama-3.1, and GLM-4)<n>Results show that SFT provides the most consistent improvements, while preference-based alignment (using DPO and KTO
• Score: 25.967830220467633
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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