2025
EMNLP
EMNLP 2025
STARLING at TSAR 2025 Shared Task Leveraging Alternative Generations for Readability Level Adjustment in Text Simplification
Abstract
AbstractReadability adjustment is crucial in text simplification, as it allows to generate language appropriate to the needs of a particular group of readers. Here we present a method for simplifying a text fragment that aims for a given CEFR level, e.g. A2 or B1. The proposed approach combines prompted large language model with sentence-level adjustment of difficulty level. The work is evaluated within the framework of TSAR 2025 shared task, showing a trade-off between precise readability adjustment and faithful meaning preservation.
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Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning and Natural Language Processing
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Keyword Pioneer
— readability adjustment
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio