Document-Level Text Simplification in Estonian Using Large Language Models

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.

12 pages, 2 figures, 2 tables. Published at LREC 2026

Computation and Language (cs.CL)

arXiv:2610.10378 [cs.CL]

(or arXiv:2610.10378v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2610.10378 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

Proceedings of the Fifteenth Language Resources and Evaluation Conference (LREC 2026), pp. 7225-7235, 2026

https://doi.org/10.63317/3ndqrcjckj32 Focus to learn more DOI(s) linking to related resources

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