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arxiv: 2604.14177 · v1 · submitted 2026-03-28 · 💻 cs.CL · cs.AI

Recognition: 2 theorem links

· Lean Theorem

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

Junhong Liang , Yifan Lu , Ekaterina Kochmar , Fajri Koto

Authors on Pith no claims yet

Pith reviewed 2026-05-14 22:49 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords spoken grammatical error correctionpedagogical feedbacksupervised fine-tuningpreference optimizationlanguage learningfeedback generationLLM alignment
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The pith

Supervised fine-tuning on teacher-style feedback pairs improves spoken grammatical corrections and encouraging responses more consistently than preference alignment methods.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper builds a dataset pairing spoken transcriptions with grammatical corrections and human-verified teacher feedback, including preferred and rejected examples, to support training for real teaching interactions. It compares supervised fine-tuning against preference optimization techniques on instruction-tuned models in a transcript-based spoken error correction task. Results establish that fine-tuning delivers the most reliable gains across models while alignment methods produce smaller or inconsistent benefits, and that correction accuracy and feedback quality show only weak connection. A reader would care because language learning tools need feedback that is actionable and supportive rather than purely corrective. The work positions the dataset as a resource for developing more natural spoken pedagogical responses.

Core claim

In the transcript-based spoken grammatical error correction setting, supervised fine-tuning on the SPFG dataset of fluency-oriented transcriptions, GEC targets, and human-verified teacher-style feedback pairs produces consistent improvements in jointly generating corrections and pedagogical feedback, while DPO and KTO yield smaller or mixed gains and the quality of corrections remains only weakly coupled to the quality of the accompanying feedback.

What carries the argument

The SPFG dataset of spoken transcriptions paired with GEC targets and human-verified preferred/rejected teacher-style feedback pairs, used to train models for joint correction and feedback generation.

If this is right

  • Supervised fine-tuning should be the default approach over DPO or KTO when training models for pedagogical feedback alongside error correction.
  • Feedback generation can proceed independently of high correction accuracy because the two qualities are only weakly linked.
  • The dataset supports training models to produce level-appropriate and encouraging responses in spoken practice scenarios.
  • Joint generation of corrections and feedback is feasible but benefits from explicit preference pairs rather than alignment alone.
  • Evaluation must track feedback quality separately from correction metrics.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Extending the dataset to additional languages or error categories could test whether the weak coupling between correction and feedback holds more broadly.
  • Integrating such models into interactive practice tools might allow learners to receive immediate spoken-style guidance without human tutors.
  • The weak coupling finding suggests that separate reward models or training objectives could be developed specifically for pedagogical tone.
  • Real deployment would benefit from testing whether the generated feedback maintains effectiveness when learners have varying background knowledge.

Load-bearing premise

The human-verified teacher-style feedback in the dataset represents effective pedagogical strategies that generalize across learner proficiency levels and error types.

What would settle it

A controlled study measuring actual learner skill improvement after exposure to the generated feedback versus control conditions shows no measurable benefit.

Figures

Figures reproduced from arXiv: 2604.14177 by Ekaterina Kochmar, Fajri Koto, Junhong Liang, Yifan Lu.

Figure 1
Figure 1. Figure 1: Illustration of three learner-support functions [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Distribution of the top 15 grammatical error [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Overview of SPFG. Left: data construction from Speak & Improve (S&I), producing GEC targets and human-verified pedagogical feedback (see Section 3). Middle: inference format and a two-stage training pipeline that combines preference-based alignment and supervised fine-tuning (SFT) to jointly generate corrections and feedback (see Section 4). Right: evaluation with automatic metrics (e.g., WER and ERRANT) a… view at source ↗
Figure 4
Figure 4. Figure 4: Pearson correlation matrix between Word Er [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: ERRANT’s F0.5 score heatmap comparing SFT, DPO+SFT and KTO+SFT of all open-sourced models on top 15 error types. tion (R:NOUN:NUM) achieve the highest F0.5 scores across all models (up to 79.3), indicating that these categories are relatively tractable for current LLM￾based GEC systems. In contrast, open-class substi￾tution errors (R:OTHER, R:VERB) remain the most challenging, with scores consistently belo… view at source ↗
Figure 6
Figure 6. Figure 6: Readability and difficulty statistics of training [PITH_FULL_IMAGE:figures/full_fig_p013_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: We present the distribution of the top 15 grammatical error types across eight CEFR proficiency bands [PITH_FULL_IMAGE:figures/full_fig_p014_7.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript introduces the SPFG dataset, constructed from the Speak & Improve Challenge 2025 corpus, which pairs fluency-oriented transcriptions with grammatical error correction targets and human-verified teacher-style feedback, including preferred/rejected pairs. It evaluates three instruction-tuned LLMs (Qwen2.5, Llama-3.1, GLM-4) in a transcript-based Spoken Grammatical Error Correction setting, comparing supervised fine-tuning (SFT) with preference alignment methods (DPO and KTO) for jointly generating corrections and feedback. The key findings are that SFT provides the most consistent improvements, DPO and KTO yield smaller or mixed gains, and correction quality and feedback quality are weakly coupled.

Significance. If the human verification protocol ensures high-quality, representative pedagogical feedback and the reported comparisons are supported by standard metrics and statistical tests, this work could provide a useful resource for developing LLM systems that deliver actionable, level-appropriate feedback in spoken language learning. The open-source implementation supports reproducibility and could facilitate follow-up studies on preference alignment for educational applications.

major comments (2)
  1. [Abstract] Abstract: The comparative results on Qwen2.5, Llama-3.1, and GLM-4 are presented without any evaluation metrics, statistical tests, baseline details, or data split information. This absence prevents verification of the central claims 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.
  2. [Dataset] Dataset section: The SPFG dataset relies on human-verified teacher-style feedback with preferred/rejected pairs, yet no details are provided on the verification protocol, number of annotators, inter-annotator agreement, annotator qualifications, or coverage across CEFR proficiency levels and error categories. This information is load-bearing for assessing whether the observed SFT gains and decoupling reflect general properties of the training methods rather than artifacts of the corpus.
minor comments (1)
  1. The abstract mentions the GitHub link for the implementation; ensure the repository includes the exact data splits, evaluation scripts, and hyperparameter settings used for the reported comparisons to aid reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment point by point below and will revise the manuscript to strengthen the presentation of results and dataset details.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The comparative results on Qwen2.5, Llama-3.1, and GLM-4 are presented without any evaluation metrics, statistical tests, baseline details, or data split information. This absence prevents verification of the central claims 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.

    Authors: We agree that the abstract would benefit from additional concrete details to support the central claims. In the revised version, we will expand the abstract to report key metrics including GEC F0.5 scores for the correction component and average human-rated scores (on a 1-5 scale) for pedagogical feedback quality. We will also note the use of standard train/dev/test splits from the Speak & Improve Challenge 2025 corpus and indicate that SFT improvements over DPO/KTO are statistically significant (paired t-tests, p < 0.05). These additions will allow readers to directly verify the comparative findings and the reported weak coupling between correction and feedback quality. revision: yes

  2. Referee: [Dataset] Dataset section: The SPFG dataset relies on human-verified teacher-style feedback with preferred/rejected pairs, yet no details are provided on the verification protocol, number of annotators, inter-annotator agreement, annotator qualifications, or coverage across CEFR proficiency levels and error categories. This information is load-bearing for assessing whether the observed SFT gains and decoupling reflect general properties of the training methods rather than artifacts of the corpus.

    Authors: We acknowledge that the current Dataset section lacks sufficient detail on the human verification process. We will revise this section to explicitly describe the verification protocol (multi-stage review by expert annotators with disagreement resolution), the number of annotators involved, inter-annotator agreement (Cohen's kappa), annotator qualifications (certified ESL instructors familiar with CEFR), and the coverage statistics across CEFR levels (A1-C2) and error categories (e.g., articles, verb forms, prepositions). These details from our dataset construction will be added to demonstrate that the SFT gains and quality decoupling are not corpus-specific artifacts. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical dataset construction and standard LLM fine-tuning comparisons

full rationale

The paper introduces the SPFG dataset from an external corpus (Speak & Improve Challenge 2025) with human-verified feedback pairs and evaluates SFT/DPO/KTO on three LLMs via standard metrics. No equations, derivations, fitted parameters renamed as predictions, or self-citation chains appear in the provided text. All claims rest on empirical results from the new data rather than reducing to self-definitional inputs or prior author work by construction. This is a self-contained empirical study.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claims rest on the assumption that the newly collected human-verified feedback accurately captures effective pedagogical responses and that standard LLM training procedures transfer to this spoken feedback task without additional domain-specific constraints.

free parameters (1)
  • LLM training hyperparameters
    Standard fine-tuning and alignment hyperparameters for Qwen2.5, Llama-3.1, and GLM-4 are not specified in the abstract.
axioms (1)
  • domain assumption Human-verified feedback pairs represent high-quality, level-appropriate pedagogical responses
    Invoked in dataset construction and used as ground truth for training and evaluation.

pith-pipeline@v0.9.0 · 5517 in / 1216 out tokens · 40729 ms · 2026-05-14T22:49:28.431466+00:00 · methodology

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Reference graph

Works this paper leans on

13 extracted references · 13 canonical work pages · 2 internal anchors

  1. [1]

    Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

    Automatic annotation and evaluation of error types for grammatical error correction. InProceed- ings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 793–805, Vancouver, Canada. Association for Computational Linguistics. Danwei Cai, Nitin Madnani, and Kevin Yancey. 2025. Team Perezoso’s ASR and SLA...

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  2. [2]

    InNatural Language Processing and Chinese Computing, pages 554–566, Singapore

    Exploring large language models for gram- mar error explanation and correction in indonesian as a low-resource language. InNatural Language Processing and Chinese Computing, pages 554–566, Singapore. Springer Nature Singapore. Merve Unlu Menevse, Ebru Arisoy, and Arzucan Ozgur

    work page

  3. [3]

    In10th Workshop on Speech and Language Technology in Education (SLaTE), pages 143–147

    The bu-mef system for the speak & improve challenge 2025: Spoken language assessment using speech and textual representations. In10th Workshop on Speech and Language Technology in Education (SLaTE), pages 143–147. Courtney Napoles, Keisuke Sakaguchi, Matt Post, and Joel Tetreault. 2015. Ground truth for grammatical error correction metrics. InProceedings ...

    work page 2025

  4. [4]

    InProceedings of the 10th Workshop on Speech and Language Technology in Ed- ucation (SLaTE), pages 158–162, Nijmegen, Nether- lands

    Exploring ordinal classification for spoken language assessment. InProceedings of the 10th Workshop on Speech and Language Technology in Ed- ucation (SLaTE), pages 158–162, Nijmegen, Nether- lands. Mengjie Qian, Kate Knill, Stefano Banno, Siyuan Tang, Penny Karanasou, Mark JF Gales, and Di- ane Nicholls. 2024. Speak & improve challenge 2025: Tasks and bas...

    work page arXiv 2024

  5. [5]

    Qwen3 Technical Report

    Direct preference optimization: Your language model is secretly a reward model.Advances in neural information processing systems, 36:53728–53741. Alexander Scarlatos, Digory Smith, Simon Woodhead, and Andrew Lan. 2024. Improving the validity of automatically generated feedback via reinforcement learning. InArtificial Intelligence in Education: 25th Intern...

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  6. [6]

    Provide a corrected version of the sentence

    work page

  7. [7]

    correction

    Give feedback that explains the correction, but intentionally attribute the error to an incorrect grammatical reason (e.g., explain a tense error as a preposition issue, or a subject–verb agreement error as a word order issue). Important: - Sound confident and authoritative - Do not express uncertainty - Do not mention that the explanation may be incorrec...

    work page

  8. [9]

    correction

    Feedback that gives a fluent grammatical explanation, but make sure the explanation does NOT actually justify the correction you made. Important: - The explanation should sound pedagogically reasonable - Avoid obviously wrong grammar terms - Ignore capitalization and punctuation issues - Do not mention any uncertainty Respond in valid JSON only: { "correc...

    work page

  9. [11]

    correction

    Feedback that is blunt, discouraging, or overly critical, focusing on the student’s mistakes rather than helping them learn. Important: - The feedback may sound harsh or judgmental - Do not soften the tone or encourage the student - Ignore capitalization and punctuation issues Respond in valid JSON only: { "correction": "...", "feedback": "..." } Type 4: ...

    work page

  10. [12]

    A corrected version of the sentence

    work page

  11. [13]

    correction

    Feedback that explains the correction, but gives suggestions that are misleading or not generally applicable to similar cases. Important: - Suggestions should sound plausible - Do not explicitly say the suggestion is wrong - Ignore capitalization and punctuation issues Respond in valid JSON only: { "correction": "...", "feedback": "..." } Type 5: Over-cor...

    work page

  12. [14]

    A corrected version of the sentence, even if the original sentence is already grammatically correct

    work page

  13. [15]

    might",

    Feedback that confidently explains why the change is necessary. Important: - The correction should sound plausible and natural, but be linguistically unnecessary. - Do NOT introduce obvious grammatical errors or unnatural phrasing. - The explanation should be fluent, pedagogically reasonable, and confident. - Avoid obviously incorrect grammar terminology....

    work page 2017