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SimpCue: Cue-Based Prompting for Multilingual Text Simplification

Mehrzad Tareh, Horacio Saggion, Stefan Bott

arXiv:2608.28042v1cs.CLcs.AI

TL;DR

The paper examines whether adding explicit linguistic information about sentence complexity improves prompt-based multilingual Easy-to-Read simplification. It compares baseline, expert-cue, and automatically predicted-cue prompting with Qwen3-8B for Catalan, Spanish, and Italian, finding that predicted cues achieve the highest overall scores but only modest, metric- and language-dependent gains.

  • Problem

    It remains unclear whether explicit linguistic cues improve multilingual prompt-based Easy-to-Read simplification beyond a general instruction.

  • Method

    The study compares baseline, gold-cue, and predicted-cue prompts for Catalan, Spanish, and Italian using Qwen3-8B.

  • Results

    Predicted-cue prompting achieves the highest pooled scores across SARI, BLEU, chrF, and BERTScore, while gold-cue prompting shows no consistent advantage over baseline.

  • Takeaways & Limitations

    Cue-based prompting can influence multilingual Easy-to-Read outputs, but its benefits are modest, metric-dependent, and language-dependent.

  • Takeaways & Limitations

    The evaluation uses automatic metrics and a single reference without human or target-reader assessment, and all experiments use one open-weight instruction model.

Abstract

from arXiv · show

Text simplification aims to make complex texts easier to understand while preserving their original meaning. Recent large language models can perform simplification through prompting, but it remains unclear whether adding explicit linguistic information about sentence complexity to the prompt improves their outputs. We investigate this question for multilingual sentence-level Easy-to-Read simplification in Catalan, Spanish, and Italian. Using Qwen3-8B, we compare a baseline prompt, a gold-cue prompt enriched with gold linguistic cues, and a predicted-cue prompt enriched with automatically predicted cues. We evaluate the outputs using SARI, BLEU, chrF, and BERTScore, and complement this evaluation with a manual qualitative analysis. Predicted-cue prompting obtains the best overall scores across all four metrics, although the gains over the baseline are small. Gold-cue prompting does not consistently improve over the baseline, and results vary across languages. These findings indicate that cue-based prompting can influence multilingual Easy-to-Read simplification, but its benefits are modest, metric-dependent, and language-dependent.

1 Introduction

The paper asks whether explicit linguistic cues can steer multilingual Easy-to-Read simplification toward specified criteria, and tests baseline, gold-cue, and predicted-cue prompting across Catalan, Spanish, and Italian. It frames predicted cues as a practical alternative when expert annotations are unavailable, while noting that cue schemes differ across languages.

  • Text simplification reduces lexical, syntactic, and discourse complexity while preserving meaning, information, and grammatical correctness.
  • General simplification prompts leave models to infer whether difficulty arises from vocabulary, syntax, reference, numbers, or compressed ideas.The paper proposes making such sources of complexity explicit through linguistic cues.
  • Easy-to-Read simplification targets accessibility for readers who may have difficulty processing complex written language.The motivation includes public information in administrative, health, educational, and civic domains.
  • The study compares baseline prompting with gold cues from expert annotations and predicted cues automatically inferred by a language-specific method.The comparison separates the value of explicit guidance from the accuracy needed to use predicted guidance in practice.
  • SimpCue evaluates cue-based prompting for Catalan, Spanish, and Italian using Qwen3-8B and applies the same prompting paradigm across languages.The specific cues differ because the underlying datasets have different annotation structures, limiting direct cross-lingual comparability.
  • The paper evaluates whether automatically predicted cues can substitute for expert-derived cues when gold annotations are unavailable.

2 Related Work

Prior work has treated simplification as rewriting, controllable generation, and prompt-based LLM generation. SimpCue extends these directions by using instance-specific linguistic cues derived from expert complexity annotations.

  • Text Simplification: Text simplification research progressed from rules, lexical resources, statistical translation, and tree-based rewriting toward neural sequence-to-sequence models.
  • Multilingual Simplification: Easy-to-Read simplification is relevant to public, legal, institutional, educational, and health information, while non-English resources remain comparatively limited.Language-specific lexical, syntactic, and discourse properties affect complexity sources and suitable simplification strategies.
  • Controllable Text Simplification: Controllable simplification conditions generation on explicit variables that influence the type or degree of simplification.Earlier systems used control tokens for operations such as splitting, deletion, and paraphrasing, while later work predicted operations for individual inputs.
  • Controllable Text Simplification: SimpCue uses expert-derived linguistic cues to identify sentence-specific sources of difficulty and provide targeted simplification guidance.
  • Prompt-Based Simplification with LLMs: Prompt-based LLM simplification avoids task-specific fine-tuning, but general instructions and readability targets do not specify which linguistic complexities to address.SimpCue supplies gold or automatically predicted cues describing the particular sources of difficulty in each input.

3 Dataset

The study uses the iDEM Corpus, a multilingual Easy-to-Read dataset for Catalan, Spanish, and Italian containing complex sources, human E2R versions, alignments, and expert annotation criteria. The languages are processed separately because their annotation schemes and linguistic realizations differ.

  • Corpus: The evaluation uses the iDEM Corpus, a multilingual Easy-to-Read dataset covering Catalan, Spanish, and Italian.The corpus contains complex source texts and corresponding human-produced E2R versions from public and institutional domains.
  • Annotations: Expert annotation criteria are normalized into gold linguistic cue labels for the study.
  • Instance Structure: Each instance includes an original complex sentence, an aligned human E2R reference, and zero or more linguistic cue labels.The aligned E2R sentences serve as human references for evaluation.
  • Dataset Statistics: Table 1 reports documents, original sentences, E2R sentence units, and training, development, and test instances for each language.
  • Annotations: Cue labels cover lexical difficulty, technical terminology, sentence structure, lists, verbal forms, pronoun reference, voice, nominalizations, connectors, and other language-specific conventions.
  • Multilingual Processing: The three languages are processed and evaluated separately because their annotation schemes, complexity cues, and simplification realizations differ.The same underlying model and prompt design are used across languages.

4 Methodology

The study formulates multilingual sentence-level Easy-to-Read simplification for Catalan, Spanish, and Italian using Qwen3-8B with baseline, gold-cue, and predicted-cue prompts. It constructs language-specific cue representations, predicts cues as a multi-label classification task, and evaluates generated outputs against human references with four automatic metrics.

  • Task formulation: The task generates same-language Easy-to-Read outputs from original sentences, allowing one complex sentence to become several shorter sentences.Human E2R references are used only for evaluation.
  • Cue prediction: Predicted cues are produced by language-specific baselines fitted on training data and selected using development data.The predicted-cue condition treats cue assignment as multi-label classification because sentences may require multiple simplification operations.
  • Cue representation: Gold cue labels preserve separate Catalan, Spanish, and Italian criterion inventories rather than mapping them to shared cross-lingual labels.The complete inventories contain 38, 34, and 33 criteria, with 23, 33, and 30 occurring in the processed datasets, respectively.
  • Prompting conditions: The three conditions compare a baseline prompt with prompts augmented by gold annotations or automatically predicted cues.Qwen3-8B serves as the model, while Pgold and Ppred incorporate gold and predicted cues.
  • Evaluation: Outputs are evaluated against human E2R references using SARI, BLEU, chrF, and BERTScore.SARI is the primary metric because it measures keeping, deleting, and adding text relative to the source and reference.

5 Results

Cue-based prompting influences multilingual Easy-to-Read simplification, but its benefits are modest and vary by metric and language. Predicted cues achieve the strongest overall automatic scores, while qualitative analysis shows uneven uptake of cues and improved segmentation in many outputs.

  • Automatic evaluation: Predicted-cue prompting achieves the highest overall BLEU, SARI, chrF, and BERTScore scores on the complete-case test subset.The evaluation includes only instances with available, non-empty outputs for all three conditions.
  • Automatic evaluation: Gold-cue prompting improves overall BLEU, SARI, and chrF over baseline, while BERTScore is slightly lower or nearly unchanged.Thus, expert-derived cues do not consistently improve performance across all metrics.
  • Automatic evaluation: Cue effects vary across languages: in Catalan, predicted cues lead BLEU and SARI, whereas baseline remains slightly higher on chrF and BERTScore.The reported language-level results show that prompting effects are not uniform.
  • Qualitative analysis: Manual analysis finds cue-based simplifications often, but not always, better than baseline outputs, with some cues apparently not used by the model.The qualitative analysis therefore complements, rather than replaces, metric-based comparisons.
  • Qualitative analysis: Cue-based outputs tend to segment information across more sentences and can reflect instructions such as directly addressing the reader.An example contrasts impersonal wording in a baseline output with direct address in a cue-based simplification.
  • Exploratory bootstrap analysis: Exploratory bootstrap results support higher BLEU for cue conditions, but SARI differences are unclear and do not establish robust quality improvements.The authors interpret BLEU differences as metric-specific evidence of increased reference overlap; SARI is the study’s main simplification metric.

6 Conclusion and Future Directions

Predicted-cue prompting achieves the highest pooled scores across four automatic metrics, but gains are modest and language-dependent. The paper therefore presents cue-based prompting as promising yet limited, with future work targeting broader data, languages, domains, and improved cue prediction and evaluation.

  • Predicted-cue prompting achieves the highest pooled scores across all four automatic metrics on the complete test subset.
  • Gold-cue prompting does not show a consistent advantage over the baseline, while BERTScore remains nearly unchanged across conditions.
  • Cue-based prompting gains are modest and vary across Catalan, Spanish, and Italian.
  • Future work should test larger datasets, additional languages, and broader Easy-to-Read domains.
  • Further work should address rare-label representation and class imbalance in cue prediction, and complement automatic evaluation with human assessment.

7 Limitations

The study’s conclusions are constrained by limited and imbalanced data, automatic evaluation against a single reference, a single model, and exploratory statistical analysis. These factors restrict generalization and make the results indicators rather than definitive evidence of consistent improvement.

  • The relatively small, imbalanced dataset may bias cue prediction toward frequent labels and limit generalization to underrepresented categories and unseen texts.
  • Automatic metrics and a single reference may miss factual preservation, fluency, adequacy, readability, and accessibility.
  • Using one open-weight instruction model limits generalization to other models, sizes, families, and decoding settings.
  • The paired bootstrap analysis is exploratory and should indicate possible system behavior rather than provide definitive evidence of consistent improvements.

Appendix A Additional Results for Cue Prediction

Cue prediction performs slightly better with monolingual training than with combined Catalan, Spanish, and Italian training. Separate language-specific models are therefore used, while a frequency-based top-k label prior helps Catalan and Italian but not Spanish.

  • Monolingual training performs slightly better than combined CAT+ES+IT training for all three languages.
  • The final pipeline uses separate cue prediction models for each language.
  • Adding a top-k label prior based on training frequency improves Catalan and Italian performance, but not Spanish performance.

Appendix B Language-Specific Cue Inventories

The three languages use separate cue inventories derived from language-specific annotation criteria, without assuming crosslingual equivalence or one-to-one alignment.

  • Catalan, Spanish, and Italian use separate cue inventories derived from their language-specific annotation criteria.
  • The inventories are not assumed to be crosslingually equivalent or one-to-one aligned.

Appendix C Generation Configuration

The generation setup specifies Qwen3-8B decoding conditions and documents cue-prediction results and candidate cue inventories for the three languages.

  • Generation settings: Qwen3-8B generated simplifications with batch size 1, disabled sampling, and a 256-token generation limit.Baseline and gold-cue conditions used greedy decoding with a repetition penalty of 1.0; prompts used the Qwen chat template without a system message.
  • Cue prediction: Table 7 reports Micro F1 results for cue prediction using BC, defined as TF–IDF with logistic regression, with and without a top-k prior.
  • Cue inventories: Table 8 lists candidate cue inventories for Catalan, Spanish, and Italian, with 23, 33, and 30 cues occurring in the processed data, respectively.
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