SimpCue: Cue-Based Prompting for Multilingual Text Simplification

2026-08-28

AI Summary

研究通过在提示中添加语言线索来改进多语言文本简化，使用Qwen3-8B模型对比不同方法，发现预测线索提示法在四种评估指标上表现最佳。

Abstract

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.

Problem

• Text Simplification
• Linguistic Cues
• Prompting
• Multilingual
• Language Models

Innovation

• cue-based prompting
• multilingual text simplification
• linguistic cues
• predicted-cue
• gold-cue