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arxiv: 2606.21981 · v1 · pith:MD4XWDJOnew · submitted 2026-06-20 · 💻 cs.CL · cs.LG

Can LLMs Control Readability? A Multi-Dimensional Evaluation Framework for CEFR-Controlled Arabic Generation

Nour Rabih , Chatrine Qwaider , Ted Briscoe This is my paper

Pith reviewed 2026-06-26 12:02 UTC · model grok-4.3

classification 💻 cs.CL cs.LG
keywords Arabic text generationCEFR readability controlLLM promptingreadability predictionlanguage learning systemslexical constraintssyntactic profiling
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The pith

CEFR-guided prompting with lexical constraints lets LLMs generate Arabic text matching target readability levels at 0.99 agreement.

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

The paper tests whether instruction-following LLMs can produce Arabic text at precise CEFR readability levels for language learners. It builds a framework that combines different prompting methods, automatic readability scoring via the Taha-19 model, checks on vocabulary constraints, and measures of sentence complexity. Experiments show that prompts which explicitly name the CEFR level and add lexical limits achieve the closest match to reference profiles, far outperforming prompts without those controls. This matters for building tools that adapt reading material to a learner's current proficiency without manual rewriting. The focus remains on Arabic, where automatic control of difficulty has received less attention than in English.

Core claim

CEFR-guided prompting with lexical constraints achieves the highest conformity to reference linguistic profiles (0.91 cosine similarity) and near-perfect agreement with predicted readability levels (0.99), while unconstrained prompting exhibits weak control over readability.

What carries the argument

A multi-dimensional evaluation framework that combines controlled prompting, Taha-19 automatic readability prediction, lexical constraint validation, and syntactic complexity profiling to measure how well generated text matches a target CEFR level.

If this is right

  • Structured prompting with explicit CEFR instructions and lexical limits produces text whose linguistic profile closely matches reference material at the same level.
  • Removing those constraints causes the generated text to deviate from the intended readability target.
  • The combination of prompting, prediction, and profiling gives a practical way to test and improve readability control in LLMs.
  • The results support using such generators inside adaptive systems that adjust Arabic content to learner proficiency.

Where Pith is reading between the lines

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

  • Similar prompting techniques could be tested on other languages that use CEFR or comparable scales to see if the same gains appear.
  • Educational apps might generate on-demand reading passages that shift difficulty as a learner progresses without needing separate corpora.
  • Future work could check whether the same framework reveals limits when models are asked to hit very fine-grained sub-levels inside a single CEFR band.

Load-bearing premise

The Taha-19 model gives accurate readability predictions for Arabic that line up with actual CEFR levels assigned by human experts.

What would settle it

A set of generated texts scored by human CEFR raters that show low agreement with the Taha-19 predictions would indicate the framework does not reliably measure control.

Figures

Figures reproduced from arXiv: 2606.21981 by Chatrine Qwaider, Nour Rabih, Ted Briscoe.

Figure 1
Figure 1. Figure 1: Taha-19 readability scores alignment with [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Mean of Syntactic features across CEFR levels. (a) shows the progression of dependency [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Distribution of mean dependency tree depths across CEFR levels [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Lexical and surface-level features across [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Example of CEFR-aligned essay prompts with vocabulary lists. [PITH_FULL_IMAGE:figures/full_fig_p013_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Prompts used for controlled Arabic text generation at different readability levels. [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗
read the original abstract

While Large Language Models (LLMs) can generate fluent Arabic text, their ability to reliably control readability levels remains unclear. We propose a multi-dimensional evaluation framework for Common European Framework of Reference for Language (CEFR)-controlled Arabic text generation, assessing whether instruction-following LLMs can serve as reliable generators for adaptive language learning. Our framework integrates controlled prompting, automatic readability prediction using a validated Taha-19 model, lexical constraint validation, and syntactic complexity profiling. Results show that structured prompting substantially improves CEFR alignment. In particular, CEFR-guided prompting with lexical constraints achieves the highest conformity to reference linguistic profiles (0.91 cosine similarity) and near-perfect agreement with predicted readability levels (0.99), while unconstrained prompting exhibits weak control. These findings establish an empirical foundation for integrating readability-aware Arabic text generation into adaptive educational systems.

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 / 0 minor

Summary. The paper proposes a multi-dimensional evaluation framework for CEFR-controlled Arabic text generation with LLMs. It integrates CEFR-guided prompting (with and without lexical constraints), automatic readability prediction via the Taha-19 model, lexical constraint validation, and syntactic complexity profiling. The central empirical claim is that structured prompting substantially improves CEFR alignment, with CEFR-guided prompting plus lexical constraints achieving 0.91 cosine similarity to reference linguistic profiles and 0.99 agreement with predicted readability levels, while unconstrained prompting shows weak control.

Significance. If the Taha-19 predictor is shown to be independently validated against human CEFR judgments, the work would supply a useful empirical foundation for readability-aware generation in Arabic educational applications. The multi-dimensional framing (prompting + lexical + syntactic) is a constructive step beyond single-metric evaluations, but the current manuscript supplies insufficient experimental detail and validation evidence to support the quantitative claims at the reported level of precision.

major comments (2)
  1. [Abstract] Abstract: The headline results (0.99 agreement with predicted levels; 0.91 cosine similarity) are obtained entirely by comparing generated text against outputs of the Taha-19 model, which is also used to set the prompting targets. The abstract asserts that Taha-19 is “validated” but reports no correlation with human CEFR labels, inter-annotator agreement, or held-out test statistics; this makes the conformity metric circular and prevents the reader from assessing whether the framework actually measures CEFR control.
  2. [Abstract] Abstract and experimental description: No information is supplied on dataset size, number of generations per condition, choice of LLM, temperature settings, statistical significance tests, or error analysis. Without these details the quantitative claims cannot be reproduced or stress-tested, directly undermining the assertion that the framework establishes an “empirical foundation” for adaptive systems.

Simulated Author's Rebuttal

2 responses · 0 unresolved

Thank you for the referee's insightful comments. We address each major comment below and plan to revise the manuscript to incorporate additional details and clarifications.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The headline results (0.99 agreement with predicted levels; 0.91 cosine similarity) are obtained entirely by comparing generated text against outputs of the Taha-19 model, which is also used to set the prompting targets. The abstract asserts that Taha-19 is “validated” but reports no correlation with human CEFR labels, inter-annotator agreement, or held-out test statistics; this makes the conformity metric circular and prevents the reader from assessing whether the framework actually measures CEFR control.

    Authors: The Taha-19 model serves as the core automatic readability predictor for both setting CEFR targets in prompting and evaluating the generated text's alignment. The manuscript refers to it as validated based on its original development. We acknowledge the need for explicit validation evidence in this context. In the revised manuscript, we will add references to the Taha-19 paper's validation results, including any reported correlations with human CEFR judgments or accuracy metrics, to mitigate concerns about circularity and allow assessment of the framework's validity for CEFR control. revision: yes

  2. Referee: [Abstract] Abstract and experimental description: No information is supplied on dataset size, number of generations per condition, choice of LLM, temperature settings, statistical significance tests, or error analysis. Without these details the quantitative claims cannot be reproduced or stress-tested, directly undermining the assertion that the framework establishes an “empirical foundation” for adaptive systems.

    Authors: We agree that the abstract is brief and omits key experimental parameters. While the full manuscript details the LLMs employed and aspects of the generation process, we will revise the abstract, methods, and results sections to explicitly report the dataset size, number of generations per condition, LLM choices, temperature settings, statistical significance tests applied, and error analysis. These enhancements will improve reproducibility and support the empirical claims. revision: yes

Circularity Check

0 steps flagged

No significant circularity; evaluation uses external Taha-19 model and reference profiles

full rationale

The paper's core results (0.91 cosine similarity to reference profiles, 0.99 agreement with predicted levels) are computed against an external, cited Taha-19 readability model and independent reference linguistic profiles. No equations, prompting targets, or metrics are shown to be defined in terms of the generated outputs or fitted by the authors themselves. The abstract explicitly labels Taha-19 as 'validated' and external; no self-citation chain, self-definitional loop, or fitted-input-renamed-as-prediction appears in the derivation. This matches the default expectation of a non-circular evaluation framework.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that Taha-19 is a reliable proxy for CEFR levels and that reference linguistic profiles accurately represent target CEFR bands; no free parameters or invented entities are evident from the abstract.

axioms (1)
  • domain assumption Taha-19 model accurately predicts CEFR-aligned readability for generated Arabic text
    Invoked to validate output conformity; stated as validated but details absent from abstract.

pith-pipeline@v0.9.1-grok · 5680 in / 1111 out tokens · 15508 ms · 2026-06-26T12:02:22.409622+00:00 · methodology

discussion (0)

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Reference graph

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