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    <abstract>This paper presents a study on readability-controlled Sentence Simplification for Italian, addressing the scarcity of annotated resources for low-resource languages. We introduce IMPaCTS (Italian Multilevel Parallel Corpus for Text Simplification), the first fully automatically created corpus of 1,444,160 original–simple sentence pairs automatically annotated with readability levels and linguistic features. It was generated using an Italian LLM prompted in zero-shot to produce multiple simplifications per input sentence. Increasing portions of the resource are used to fine-tune mono- and multilingual open-weight LLMs, conditioning them to generate simplifications at a target readability level. Results from automatic and human evaluations show that fine-tuning on IMPaCTS improves performance both in terms of task completion and adherence to the targeted readability levels compared to few-shot baselines.</abstract>
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%0 Conference Proceedings
%T Controllable Sentence Simplification in Italian: Fine-Tuning Large Language Models on Automatically Generated Resources
%A Papucci, Michele
%A Venturi, Giulia
%A Dell’Orletta, Felice
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F papucci-etal-2026-controllable
%X This paper presents a study on readability-controlled Sentence Simplification for Italian, addressing the scarcity of annotated resources for low-resource languages. We introduce IMPaCTS (Italian Multilevel Parallel Corpus for Text Simplification), the first fully automatically created corpus of 1,444,160 original–simple sentence pairs automatically annotated with readability levels and linguistic features. It was generated using an Italian LLM prompted in zero-shot to produce multiple simplifications per input sentence. Increasing portions of the resource are used to fine-tune mono- and multilingual open-weight LLMs, conditioning them to generate simplifications at a target readability level. Results from automatic and human evaluations show that fine-tuning on IMPaCTS improves performance both in terms of task completion and adherence to the targeted readability levels compared to few-shot baselines.
%R 10.63317/5fgm358dfxt5
%U https://aclanthology.org/2026.lrec-1.570/
%U https://doi.org/10.63317/5fgm358dfxt5
%P 7178-7191

Markdown (Informal)

[Controllable Sentence Simplification in Italian: Fine-Tuning Large Language Models on Automatically Generated Resources](https://aclanthology.org/2026.lrec-1.570/) (Papucci et al., LREC 2026)

• Controllable Sentence Simplification in Italian: Fine-Tuning Large Language Models on Automatically Generated Resources (Papucci et al., LREC 2026)

ACL

• Michele Papucci, Giulia Venturi, and Felice Dell’Orletta. 2026. Controllable Sentence Simplification in Italian: Fine-Tuning Large Language Models on Automatically Generated Resources. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 7178–7191, Palma de Mallorca, Spain. ELRA Language Resource Association.