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

arXiv: https://arxiv.org/abs/2604.14177 · HTML: https://mlsanity.com/paper/2604.14177/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Process reward models for reasoning, one of 10 directions in LLM alignment / RLHF / preference optimisation.
- Builds on 8 of its 28 references that are in the map.
- First posted on arXiv 28 Mar 2026.

## Placed in

- LLM alignment / RLHF / preference optimisation / [Process reward models for reasoning](https://mlsanity.com/alignment/process-reward-models-for-reasoning/)

## Builds on (most cited first, 8 of 8)

- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [CLASS: A Design Framework for building Intelligent Tutoring Systems based on Learning Science principles](https://mlsanity.com/paper/2305.13272/) (2023)
- [AutoTutor meets Large Language Models: A Language Model Tutor with Rich Pedagogy and Guardrails](https://mlsanity.com/paper/2402.09216/) (2024)
- [Improving the Validity of Automatically Generated Feedback via Reinforcement Learning](https://mlsanity.com/paper/2403.01304/) (2024)
- [Inherent Biases in Reference based Evaluation for Grammatical Error Correction and Text Simplification](https://mlsanity.com/paper/1804.11254/) (2018)
- [Speak & Improve Challenge 2025: Tasks and Baseline Systems](https://mlsanity.com/paper/2412.11985/) (2024)
- [Natural Language-based Assessment of L2 Oral Proficiency using LLMs](https://mlsanity.com/paper/2507.10200/) (2025)
- [Leveraging What's Overfixed: Post-Correction via LLM Grammatical Error Overcorrection](https://mlsanity.com/paper/2509.20811/) (2025)

## Abstract (arXiv)

Grammatical error correction (GEC) and explanation (GEE) have made rapid progress, but real teaching scenarios also require learner-friendly pedagogical feedback that is actionable, level-appropriate, and encouraging. We introduce SPFG (Spoken Pedagogical Feedback Generation), a dataset built based on the Speak & Improve Challenge 2025 corpus, pairing fluency-oriented transcriptions with GEC targets and 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.