arxiv: 2604.22542 · v1 · submitted 2026-04-24 · 💻 cs.CL · cs.AI

Recognition: unknown

Controllable Spoken Dialogue Generation: An LLM-Driven Grading System for K-12 Non-Native English Learners

Haidong Yuan , Haokun Zhao , Wanshi Xu , Songjun Cao , Qingyu Zhou , Long Ma , Hongjie Fan

Authors on Pith no claims yet

Pith reviewed 2026-05-08 11:40 UTC · model grok-4.3

classification 💻 cs.CL cs.AI

keywords spoken dialogue generationLLM adaptationK-12 language learningproficiency gradingpolicy optimizationeducational dialogue systemscurriculum-aligned generationmulti-turn dialogue

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The pith

A new algorithm lets language models generate spoken dialogues graded exactly to K-12 learner proficiency levels.

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

The paper introduces a framework that aligns large language model outputs with the abilities of non-native K-12 English learners by using China's curriculum standards to grade vocabulary and dialogues. It supplies graded word lists and a dialogue corpus, then applies the DDPO algorithm to optimize the generated conversations for naturalness, teaching value, and variety at the same time. A reader would care because the approach offers a concrete method to create personalized speaking practice materials in settings without immersion or enough qualified teachers.

Core claim

The authors claim that the DDPO algorithm, a multi-turn GRPO-based policy optimization method, enables LLMs to produce controllable spoken dialogues that match specific proficiency tiers, resulting in low rates of out-of-vocabulary words, high lexical and structural diversity, and improved scores for conversational naturalness and pedagogical effectiveness relative to conventional generation techniques.

What carries the argument

The DDPO (Diversity Driven Policy Optimization) algorithm, a reinforcement learning procedure that extends GRPO to multi-turn settings in order to jointly maximize dialogue quality while preserving diversity and alignment to target vocabulary tiers.

Load-bearing premise

That a four-tier grading system drawn from national curriculum standards can accurately sort words and dialogues by the lexical complexity appropriate for each K-12 grade level.

What would settle it

A direct comparison showing that dialogues produced by the DDPO method contain more out-of-vocabulary words for the stated grade level or receive lower human ratings for naturalness and teaching value than standard LLM prompting baselines.

Figures

Figures reproduced from arXiv: 2604.22542 by Haidong Yuan, Haokun Zhao, Hongjie Fan, Long Ma, Qingyu Zhou, Songjun Cao, Wanshi Xu.

**Figure 2.** Figure 2: Data generation pipeline: Step 1, vocabulary, grammar, and proficiency information are extracted from view at source ↗

**Figure 3.** Figure 3: Overview of the Diversity Driven Policy Optimization (DDPO) framework. Given a fixed dialogue view at source ↗

**Figure 4.** Figure 4: The train dataset covers dialogues from L1 view at source ↗

**Figure 5.** Figure 5: Proportion of dialogues with OOV. Compared with the base model and SFT method, the DDPO method significantly reduces the OOV at each level. overall dialogue quality (Average 4.30 and 4.34, respectively). Meanwhile, DDPO maintains relatively good diversity. Comparison of Different Training Methods As shown in view at source ↗

read the original abstract

Large language models (LLMs) often fail to meet the pedagogical needs of K-12 English learners in non-native contexts due to a proficiency mismatch. To address this widespread challenge, we introduce a proficiency-aligned framework that adapts LLM outputs to learner abilities, using China's national curriculum (CSE) as a representative case. Our framework enables precise control over lexical complexity through a four-tier grading system, supported by a comprehensive suite of new resources: graded vocabulary lists and a multi-turn dialogue corpus. Our core technical contribution is the \textbf{DDPO} algorithm,Diversity Driven Policy Optimization, a multi-turn GRPO-based approach designed to preserve dialogue diversity while holistically optimizing dialogue quality. This method significantly outperforms conventional approaches, achieving low out-of-vocabulary rates and high diversity while enhancing conversational naturalness and pedagogical value. While grounded in the CSE, our framework is designed for flexibility and can be readily adapted to other educational standards. Our models, data, and code will all be open-sourced, providing a scalable platform for personalized English speaking practice that effectively addresses the unique challenges faced by K-12 learners in non-immersive environments.

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.

Desk Editor's Note private letter to a colleague

The paper gives a practical framework with DDPO and CSE-based resources to generate learner-appropriate dialogues for K-12 non-native English speakers, but its performance claims rest on details that are not visible in the abstract.

read the letter

The main thing here is a system that tries to fix how LLMs produce dialogues too advanced for young kids learning English outside immersive settings. They define a four-tier grading system drawn from China's CSE, build supporting vocabulary lists and a multi-turn corpus, and introduce DDPO as a multi-turn extension of GRPO that adds a diversity term to the reward. The plan to release models, data, and code is straightforward and useful for anyone who wants to adapt it to other standards or languages. That combination of concrete resources and an open release is the part that stands out as immediately usable. The application itself is timely; personalized speaking practice tools for K-12 learners are a clear need and this framing makes the control mechanism explicit rather than relying on vague prompting. The soft spots sit in the optimization and validation. The abstract asserts that DDPO delivers low out-of-vocabulary rates, high diversity, naturalness, and pedagogical value all at once without major trade-offs, yet no numbers, baselines, or component tests are shown. The stress-test concern about reward balancing in the multi-turn GRPO setup is on point: without ablations or Pareto curves it is hard to see how the diversity regularizer actually prevents the usual collapse when quality and pedagogical terms are added. The grading control is also only as strong as the enforcement step, and if that step is just prompting the model can still leak higher-tier words. This work is aimed at researchers building educational dialogue systems or controllable generation methods for constrained domains. A reader who needs ideas for aligning LLM output to proficiency levels or who wants reusable graded resources would get something concrete from it. I would send it to peer review because the problem is well-motivated, the resources add value even if the method needs tightening, and the open-source commitment makes follow-up work feasible.

Referee Report

3 major / 2 minor

Summary. The paper introduces a proficiency-aligned framework for generating spoken dialogues with LLMs for K-12 non-native English learners, using China's CSE curriculum as a case. It proposes a four-tier grading system for lexical complexity, supported by new resources including graded vocabulary lists and a multi-turn dialogue corpus. The core contribution is the DDPO (Diversity Driven Policy Optimization) algorithm, a multi-turn GRPO-based method intended to preserve dialogue diversity while optimizing quality, naturalness, and pedagogical value. The authors claim this significantly outperforms conventional approaches with low OOV rates and high diversity, and note that the framework is adaptable to other standards, with all models, data, and code to be open-sourced.

Significance. If the empirical results hold, the work could meaningfully advance controllable text generation for educational applications by addressing LLM proficiency mismatch in language learning. The curriculum-aligned grading combined with a diversity-aware RL method offers a practical approach to personalized practice materials. Open-sourcing the graded resources and code would be a clear strength, supporting reproducibility and adaptation. The flexibility to other standards broadens potential impact in the intersection of NLP and education technology.

major comments (3)

[Abstract] Abstract: the claim that DDPO 'significantly outperforms conventional approaches, achieving low out-of-vocabulary rates and high diversity while enhancing conversational naturalness and pedagogical value' is presented with no quantitative metrics, baseline comparisons, or result tables. This absence makes it impossible to evaluate whether the central outperformance assertion is supported.
[DDPO algorithm description] DDPO algorithm description: the multi-turn GRPO formulation is asserted to 'preserve dialogue diversity while holistically optimizing dialogue quality' without trade-offs, yet no details are supplied on the reward function (e.g., how any diversity term is added to the GRPO objective) or component ablations/Pareto curves demonstrating that quality optimization does not induce mode collapse or reduced lexical variety.
[Framework description] Framework description: the four-tier CSE grading system is said to enable 'precise control over lexical complexity', but the enforcement mechanism (prompting, constrained decoding, or post-filtering) is unspecified. Any leakage would directly undermine the controllability premise that underpins the entire resource suite and evaluation.

minor comments (2)

[Abstract] Abstract: the acronym GRPO appears without expansion on first use; it should be defined (presumably Group Relative Policy Optimization or similar) for clarity.
[Abstract] Abstract: the statement that 'our models, data, and code will all be open-sourced' would benefit from a placeholder repository link or DOI even at submission stage.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive and detailed comments, which highlight areas where the manuscript can be strengthened for clarity and rigor. We address each major comment point by point below. Revisions have been made to incorporate the requested details and supporting evidence without altering the core contributions.

read point-by-point responses

Referee: [Abstract] Abstract: the claim that DDPO 'significantly outperforms conventional approaches, achieving low out-of-vocabulary rates and high diversity while enhancing conversational naturalness and pedagogical value' is presented with no quantitative metrics, baseline comparisons, or result tables. This absence makes it impossible to evaluate whether the central outperformance assertion is supported.

Authors: We agree that the abstract would benefit from explicit quantitative support for the performance claims. We have revised the abstract to include a concise summary of key experimental metrics, such as OOV rates, diversity scores, and comparative results against baselines (e.g., standard prompting and RL methods). These are now briefly stated in the abstract, with full tables, statistical significance, and analysis retained in the experimental results section. revision: yes
Referee: [DDPO algorithm description] DDPO algorithm description: the multi-turn GRPO formulation is asserted to 'preserve dialogue diversity while holistically optimizing dialogue quality' without trade-offs, yet no details are supplied on the reward function (e.g., how any diversity term is added to the GRPO objective) or component ablations/Pareto curves demonstrating that quality optimization does not induce mode collapse or reduced lexical variety.

Authors: The current manuscript provides a high-level description of the DDPO algorithm but lacks the granular implementation details requested. We have expanded the methods section to include the precise reward function formulation, explicitly showing how a diversity regularization term is added to the multi-turn GRPO objective. We have also added new ablation studies and Pareto curve analyses in the experiments section to empirically demonstrate that quality optimization does not induce mode collapse or reduce lexical variety, with quantitative results across varying reward weights. revision: yes
Referee: [Framework description] Framework description: the four-tier CSE grading system is said to enable 'precise control over lexical complexity', but the enforcement mechanism (prompting, constrained decoding, or post-filtering) is unspecified. Any leakage would directly undermine the controllability premise that underpins the entire resource suite and evaluation.

Authors: We agree that the enforcement mechanism must be explicitly detailed to substantiate the controllability claims. We have revised the framework description to specify that lexical complexity control is achieved through constrained prompting with the graded vocabulary lists, followed by post-generation filtering and verification steps. We also describe safeguards against leakage, such as vocabulary masking and tier-compliance checks, ensuring generated dialogues remain within the target proficiency level and supporting the overall resource suite. revision: yes

Circularity Check

0 steps flagged

No circularity: DDPO presented as new GRPO extension with empirical claims

full rationale

The abstract introduces DDPO as a multi-turn GRPO-based algorithm with explicit design goals for diversity and quality optimization, supported by new graded resources and a corpus. No equations, self-definitions, or fitted parameters are shown that reduce the claimed outcomes to inputs by construction. The four-tier CSE grading and performance claims are presented as external to the method definition itself. No self-citation chains or ansatz smuggling appear in the provided text. The derivation chain remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The abstract does not provide enough technical detail to identify specific free parameters, axioms, or invented entities beyond standard assumptions in LLM adaptation.

pith-pipeline@v0.9.0 · 5522 in / 1185 out tokens · 62221 ms · 2026-05-08T11:40:54.276945+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

29 extracted references · 1 canonical work pages · 1 internal anchor

[1]

Jingxuan Wu and Carsten Roever

Profiling English sentences based on CEFR levels.ITL-International Journal of Applied Linguis- tics (Belgium), pages 103–126. Jingxuan Wu and Carsten Roever. 2021. Proficiency and preference organization in second language mandarin chinese refusals.The Modern Language Journal, 105(4):897–918. Helen Yannakoudakis, Ted Briscoe, and Ben Medlock

2021
[2]

DAPO: An Open-Source LLM Reinforcement Learning System at Scale

A new dataset and method for automatically grading esol texts. InProceedings of the 49th annual meeting of the association for computational linguis- tics: human language technologies, pages 180–189. Qiying Yu, Zheng Zhang, Ruofei Zhu, Yufeng Yuan, Xiaochen Zuo, Yu Yue, Tiantian Fan, Gaohong Liu, Lingjun Liu, Xin Liu, and 1 others. 2025. Dapo: An open-sou...

work page internal anchor Pith review arXiv 2025
[3]

Engage in an English conversation with a student at {grade_level} on the topic: {topic}
[4]

Task Constraints

In each turn, summarize/expand on the previous input and ask a follow-up ques- tion. Task Constraints
[5]

Use vocabulary suitable for{level}

Length & Vocab:Keep answers within {word_count} words. Use vocabulary suitable for{level}
[6]

Do not use unlisted grammar

Grammar Constraint:You mayonly use the following grammar:{grammar}. Do not use unlisted grammar
[7]

Output Requirements

Adaptability:Match the student’s abil- ity level—neither too difficult nor too simple. Output Requirements
[8]

Structure:Response must include aRe- action(respond/expand) and aQuestion (open-ended follow-up)
[9]

You drink water with your lunch

Guidance:If the conversation strays, guide the student back to the topic. E Dialogue Quality Evaluation Prompt Table 6 presents the detailed system prompt used for the model-based subjective evaluation. The evaluator (e.g., GPT-4) is instructed to act as a rigorous dialogue quality assessor using a 5-point Likert scale across eight specific dimensions.Cru...
[10]

• -2: Does not align with topic

Topic Relevance (Base: 5, Deduction) • -1: Led astray by user or slight digression. • -2: Does not align with topic. -3: Completely off-topic
[11]

• -2: Non-human symbols

Task Completion (Base: 3, Add/Deduct) • -1: No question asked, wrong length, or prompt violation. • -2: Non-human symbols. -4: Chinese content. • +1: Includes summary/extension. +2: Excellent extension
[12]

• 4: 1–2 points of extra info

Information Richness (Base: 3, Addition) • 3: Basic question only. • 4: 1–2 points of extra info. 5:≥3 points of extra info
[13]

reason_consistency

Topic Guidance (Base: 3, Addition) • 3: Yes/No question. • 4: Fact-based question (item, color, etc.). • 5: Question requiring thought/elaboration. Output Format:Output strictly in JSON format: {"reason_consistency": "...", "score_consistency": int, ...} Table 6: The full prompt used for LLM-based subjective evaluation. The prompt defines specific base sc...
[14]

Finishing a big project alone is a hugeaccomplishment. How did taking on that challenge and succeeding make you feel about your owngrowthandcapabilities?

7B Base Model “Finishing a big project alone is a hugeaccomplishment. How did taking on that challenge and succeeding make you feel about your owngrowthandcapabilities?” OOV Errors:accomplishment,growth,capabilities(Level>L3)
[15]

Proud and independent, those are wonderful feelings! How do you think this experience will influence your future approachto similar challenges?

7B SFT Model “Proud and independent, those are wonderful feelings! How do you think this experience will influence your future approachto similar challenges?” OOV Error:approach(Level>L3)
[16]

Fantastic connection! Feeling proud makes a big difference. How does feeling proud make overcoming challenges feel more successful to you?

7B DDPO (Ours) “Fantastic connection! Feeling proud makes a big difference. How does feeling proud make overcoming challenges feel more successful to you?” OOV Error: None Table 7: Comparison of model responses. The Base and SFT models fail to strictly follow the vocabulary constraints, generating OOV words (marked in red). The DDPO model successfully gen...
[17]

Good habit! Do you eat rice for breakfast too?
[18]

Good for you! Do you eat rice, vegetables, and meat for dinner too?
[19]

Good habit! What’s your favorite meat to eat with rice and vegetables?
[20]

Good habit! Do you like eating fruit after lunch?
[21]

Good habit! Do you like eating fruits after lunch?
[22]

Good habit! Do you like eating fruits with your lunch too?
[23]

You drink water with your lunch

Good for you! Do you like eating fruits with your meals? GRPO 1. You drink water with your lunch. Do you eat rice with your lunch?
[24]

Do you eat rice with your lunch?

You drink water with your lunch. Do you eat rice with your lunch?
[25]

You drink water with your lunch! Do you eat meat for lunch?
[27]

You drink water with your lunch! Do you eat rice with your lunch?
[28]

You drink water with your lunch! Do you eat meat with your lunch?
[29]

Do you eat rice for lunch?

You drink water with your lunch. Do you eat rice for lunch?
[30]

The GRPO baseline collapses into a repetitive template (repeating user input + simple question), whereas DDPO generates diverse feedback and follow-up questions

You drink water with your lunch! Do you eat rice with your lunch? DDPO (Ours) 1.Great!Eating vegetables makes food taste nice! What kind of food do you like? 2.Wonderful!Water makes eating feel good! What kind of drinks do you have? 3.Wonderful!Water helps make food taste good! What makes food so nice? 4.Great!Water makes eating feel good! What kind of dr...