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        <title>Document-Level Text Simplification in Estonian Using Large Language Models</title>
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    <abstract>Document-level text simplification involves transformations that go beyond sentence-internal edits, addressing discourse coherence, anaphora resolution, and cross-paragraph consistency. Despite advances in sentence-level simplification for high-resource languages, document-level simplification in morphologically rich, low-resource languages such as Estonian remains largely unexplored. This study presents a comprehensive evaluation of five state-of-the-art multilingual large language models (LLMs) for document-level simplification in Estonian. Three prompting strategies are examined: single-pass generation, pipeline-based modular agents, and guideline-augmented pipelines. The evaluation framework integrates automatic metrics assessing readability, semantic preservation, and discourse coherence, alongside a structured manual annotation protocol. The findings indicate that Gemini-2.0 and LLaMA-3.3 produce outputs with near-native fluency and strong meaning preservation, whereas other models display notable grammatical and semantic limitations. This work contributes novel document-level coherence metrics, evidence-based prompting strategies, and publicly available resources for reproducibility.</abstract>
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%0 Conference Proceedings
%T Document-Level Text Simplification in Estonian Using Large Language Models
%A Muru, Meeri-Ly
%A Barbu, Eduard
%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 muru-barbu-2026-document
%X Document-level text simplification involves transformations that go beyond sentence-internal edits, addressing discourse coherence, anaphora resolution, and cross-paragraph consistency. Despite advances in sentence-level simplification for high-resource languages, document-level simplification in morphologically rich, low-resource languages such as Estonian remains largely unexplored. This study presents a comprehensive evaluation of five state-of-the-art multilingual large language models (LLMs) for document-level simplification in Estonian. Three prompting strategies are examined: single-pass generation, pipeline-based modular agents, and guideline-augmented pipelines. The evaluation framework integrates automatic metrics assessing readability, semantic preservation, and discourse coherence, alongside a structured manual annotation protocol. The findings indicate that Gemini-2.0 and LLaMA-3.3 produce outputs with near-native fluency and strong meaning preservation, whereas other models display notable grammatical and semantic limitations. This work contributes novel document-level coherence metrics, evidence-based prompting strategies, and publicly available resources for reproducibility.
%R 10.63317/3ndqrcjckj32
%U https://aclanthology.org/2026.lrec-1.573/
%U https://doi.org/10.63317/3ndqrcjckj32
%P 7225-7235

Markdown (Informal)

[Document-Level Text Simplification in Estonian Using Large Language Models](https://aclanthology.org/2026.lrec-1.573/) (Muru & Barbu, LREC 2026)

• Document-Level Text Simplification in Estonian Using Large Language Models (Muru & Barbu, LREC 2026)

ACL

• Meeri-Ly Muru and Eduard Barbu. 2026. Document-Level Text Simplification in Estonian Using Large Language Models. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 7225–7235, Palma de Mallorca, Spain. ELRA Language Resource Association.