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arxiv: 2604.23844 · v1 · submitted 2026-04-26 · 💻 cs.CL

Translate or Simplify First: An Analysis of Cross-lingual Text Simplification in English and French

Ido Dahan , Omer Toledano , Roey J. Gafter , Sharon Pardo , Oren Tsur , Hila Zahavi , Elior Sulem This is my paper

Pith reviewed 2026-05-08 06:07 UTC · model grok-4.3

classification 💻 cs.CL
keywords cross-lingual text simplificationprompting strategieslarge language modelsEnglish-French translationBLEU scoreslinguistic simplicity featuresmeaning preservationcomposition vs decomposition
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The pith

Direct prompting best preserves meaning in cross-lingual text simplification between English and French, while translating first then simplifying produces the simplest outputs.

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

The paper tests five prompting approaches for large language models to handle both translation and simplification at once between English and French. These include a single direct prompt for both tasks, composition prompts that chain the tasks in one response, and decomposition prompts that split the tasks across separate calls. Experiments cover Wikipedia articles and medical texts using seven models, with quality checked through BLEU scores, linguistic measures of length and complexity, and human judgments. Direct prompting scores highest on BLEU, showing stronger retention of original meaning, while the translate-then-simplify sequence produces outputs with the greatest reduction in linguistic complexity. Readers would care because these ordering choices affect how accurately and accessibly information reaches speakers of the target language.

Core claim

The authors establish that direct prompting, in which the model is told to translate and simplify in one step, consistently records the highest BLEU scores across corpora and models, indicating stronger fidelity to source meaning. By comparison, the composition approach of translating first and then simplifying within a single prompt produces the greatest simplicity gains according to linguistic features such as sentence length and word complexity. These patterns appear in both English-to-French and French-to-English directions and are supported by human ratings of simplicity and meaning preservation.

What carries the argument

The five prompting systems (direct, two composition variants, two decomposition variants) that sequence or combine translation and simplification operations, measured by BLEU for meaning fidelity and by linguistic features for simplicity.

If this is right

  • When meaning fidelity is the primary goal, a single direct prompt should be used rather than sequenced operations.
  • When maximum linguistic simplicity is required, the translate-then-simplify order outperforms both direct and simplify-first orders.
  • The performance gap between integrated and separated prompts is smaller than the gap between different task orders.
  • The observed trade-off between fidelity and simplicity holds across Wikipedia and medical genres.
  • Human ratings align with the automatic metrics on both dimensions.

Where Pith is reading between the lines

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

  • The same ordering preference may appear when simplifying and translating into other target languages or domains.
  • Prompt engineering for multilingual accessibility could prioritize the translate-then-simplify sequence when the audience needs easy reading over exact phrasing.
  • Model developers might benefit from training data that explicitly rewards the translate-first sequence for simplification tasks.
  • The findings raise the question of whether similar order effects occur in other chained LLM tasks such as summarization followed by translation.

Load-bearing premise

BLEU scores combined with the chosen linguistic features and human ratings capture meaning preservation and simplicity without systematic bias across genres or models.

What would settle it

A side-by-side human study in which raters judge semantic accuracy of direct-prompt outputs versus translate-then-simplify outputs on the same source sentences, or an independent semantic similarity metric that reverses the BLEU ranking.

Figures

Figures reproduced from arXiv: 2604.23844 by Elior Sulem, Hila Zahavi, Ido Dahan, Omer Toledano, Oren Tsur, Roey J. Gafter, Sharon Pardo.

Figure 1
Figure 1. Figure 1: Conceptual diagram of text transformation view at source ↗
read the original abstract

Cross-Lingual Text Simplification (CLTS) aims to make content more accessible across languages by simultaneously addressing both linguistic complexity and translation. This study investigates the effectiveness of different prompting strategies for CLTS between English and French using large language models (LLMs). We examine five distinct prompting systems: a direct prompt instructing the LLM to perform both translation and simplification simultaneously, two Composition approaches that either translate-then-simplify or simplify-then-translate within a single prompt, and two decomposition approaches that perform the same operations in separate, consecutive prompts. These systems are evaluated across a diverse set of five corpora of different genres (Wikipedia and medical texts) using seven state-of-the-art LLMs. Output quality is assessed through a multi-faceted evaluation framework comprising automatic metrics, comprehensive linguistic feature analysis, and human evaluation of simplicity and meaning preservation. Our findings reveal that while direct prompting consistently achieves the highest BLEU scores, indicating meaning fidelity, Translate-then-Simplify approaches demonstrate the highest simplicity, as measured by the linguistic features.

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

3 major / 2 minor

Summary. The manuscript analyzes prompting strategies for cross-lingual text simplification (CLTS) between English and French using LLMs. It compares a direct prompt (simultaneous translation and simplification), two composition approaches (translate-then-simplify and simplify-then-translate in one prompt), and two decomposition approaches (separate consecutive prompts). These are tested on five corpora spanning Wikipedia and medical genres with seven LLMs. The central claim, based on automatic metrics (BLEU), linguistic feature analysis, and human ratings, is that direct prompting achieves the highest BLEU scores (indicating meaning fidelity) while translate-then-simplify approaches yield the highest simplicity as measured by linguistic features.

Significance. If the chosen proxies are validated, the work offers practical guidance on prompting trade-offs for CLTS and demonstrates the value of multi-faceted evaluation across models and genres. The empirical breadth (seven LLMs, five corpora, human + automatic + feature analysis) is a clear strength and supports generalizability claims in LLM-based multilingual simplification.

major comments (3)
  1. [Evaluation] Evaluation section: The interpretation that highest BLEU scores indicate superior meaning fidelity is load-bearing for the headline claim but rests on an unvalidated assumption. BLEU is known to penalize the lexical substitutions, deletions, and paraphrases that define successful simplification; without per-strategy correlation analysis between BLEU and human meaning-preservation ratings (or an ablation showing BLEU tracks fidelity independently of simplification degree), the proxy cannot reliably isolate fidelity from failure to simplify.
  2. [Linguistic feature analysis] Linguistic feature analysis: The claim that translate-then-simplify produces the highest simplicity relies on surface features (sentence length, word complexity, etc.) without reported validation that these features correlate with human simplicity judgments within each corpus or after controlling for translation artifacts and model style. This correlation check is necessary to rule out confounding and is absent from the reported results.
  3. [Methodology] Methodology: Exact prompt templates, the statistical tests used to compare metric differences across strategies, and inter-annotator agreement for the human ratings are not provided. These omissions directly affect reproducibility and the ability to assess whether observed differences are robust or model-specific.
minor comments (2)
  1. [Experimental Setup] A summary table contrasting the five prompting strategies (direct vs. composition vs. decomposition) would improve clarity in the experimental setup description.
  2. [Abstract] The abstract should explicitly state the translation direction (English to French) rather than implying bidirectional CLTS.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment below, providing clarifications and committing to specific revisions that strengthen the validity and reproducibility of our claims without altering the core findings.

read point-by-point responses

  1. Referee: [Evaluation] Evaluation section: The interpretation that highest BLEU scores indicate superior meaning fidelity is load-bearing for the headline claim but rests on an unvalidated assumption. BLEU is known to penalize the lexical substitutions, deletions, and paraphrases that define successful simplification; without per-strategy correlation analysis between BLEU and human meaning-preservation ratings (or an ablation showing BLEU tracks fidelity independently of simplification degree), the proxy cannot reliably isolate fidelity from failure to simplify.

    Authors: We acknowledge BLEU's known limitations in simplification tasks, where it can penalize the very changes that improve accessibility. Our evaluation already pairs BLEU with separate human ratings of meaning preservation to provide a multi-faceted view. To directly validate the proxy, we will add per-strategy Pearson or Spearman correlations between BLEU scores and human meaning-preservation ratings (and an ablation controlling for simplification degree) in the revised manuscript. This will either support the use of BLEU for fidelity or qualify our interpretation accordingly. revision: yes

  2. Referee: [Linguistic feature analysis] Linguistic feature analysis: The claim that translate-then-simplify produces the highest simplicity relies on surface features (sentence length, word complexity, etc.) without reported validation that these features correlate with human simplicity judgments within each corpus or after controlling for translation artifacts and model style. This correlation check is necessary to rule out confounding and is absent from the reported results.

    Authors: We agree that surface linguistic features require validation against human judgments to rule out confounds such as model style or translation artifacts. In the revised manuscript we will report correlations (e.g., Pearson) between the key linguistic features and human simplicity ratings, computed within each corpus and with controls for genre and model where data permit. This will strengthen the link between the reported features and perceived simplicity. revision: yes

  3. Referee: [Methodology] Methodology: Exact prompt templates, the statistical tests used to compare metric differences across strategies, and inter-annotator agreement for the human ratings are not provided. These omissions directly affect reproducibility and the ability to assess whether observed differences are robust or model-specific.

    Authors: We apologize for these omissions. The revised manuscript will include the full set of prompt templates for all five strategies, explicit description of the statistical tests (including test type, multiple-comparison correction, and p-value thresholds), and inter-annotator agreement metrics (e.g., Krippendorff's alpha or Cohen's kappa) for the human ratings. These additions will enable full reproducibility and allow readers to assess robustness across models and genres. revision: yes

Circularity Check

0 steps flagged

No circularity: direct empirical comparisons of prompting variants

full rationale

The paper reports an empirical study of five prompting strategies for CLTS, evaluated via BLEU, linguistic features, and human judgments on five external corpora and seven LLMs. No derivations, equations, fitted parameters, or self-referential definitions appear; results are straightforward experimental outcomes against independent benchmarks. No load-bearing self-citations or uniqueness claims reduce any finding to its inputs by construction. This is a standard non-circular empirical analysis.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Empirical prompting study with no mathematical derivation; central claim rests on standard NLP evaluation assumptions rather than new axioms or entities.

axioms (1)
  • domain assumption BLEU and linguistic feature analysis reliably measure meaning fidelity and simplicity in cross-lingual outputs
    Invoked to interpret direct prompting as highest fidelity and translate-then-simplify as highest simplicity.

pith-pipeline@v0.9.0 · 5501 in / 1372 out tokens · 89163 ms · 2026-05-08T06:07:54.438650+00:00 · methodology

discussion (0)

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