It is found that cue-based prompting can influence multilingual sentence-level Easy-to-Read simplification, but its benefits are modest, metric-dependent, and language-dependent.
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.
Experiments on the OneStopEnglish corpus using multiple state-of-the-art LLMs show that incorporating retrieved examples consistently improves simplification quality over prompt-only generation and achieves competitive or superior performance compared with representative supervised (T5) and planning-based (PlanSimp) do...
Marina Litvak, Ariel Perstin, Ilan Shtilman et al.· 0 citations
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Michele Papucci, Giulia Venturi, F. Dell'orletta· International Conference on...· 0 citations
Experiments on traditional Mongolian text summarization tasks demonstrate that the proposed Nucleus-to-Periphery Retrieval-Iterative Prompting method significantly out-performs multiple baseline models on automatic evaluation metrics including ROUGE-1, ROUGE-2, and ROUGE-L.
Meng-Han Li, Ni-Er Wu, Yang Liu et al.· Proceedings of the Thirty-Fi...· 0 citations
MTEB-PT is presented, a Portuguese benchmark constructed from a subset of MMTEB, comprising 14 existing datasets across Semantic Textual Similarity (STS), classification, retrieval, and reranking, and shows that language-specific fine-tuning still improves model performance in Portuguese, especially on task types that...
Lucas H.T. Okamura, Alexandre Alcoforado, A. H. R. Costa· 1 citation
This paper tests whether prompting the same (frozen) SLM in three typologically diverse languages and aggregating the outputs can improve classification without retraining or translation, and suggests that cross-lingual diversity rather than surface-level input variation drives the gain.
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