In this paper, we present our approach for the CLEF 2025 SimpleText Task 1, which addresses both sentence-level and document-level scientific text simplification. For sentence-level simplification, our methodology employs large language models (LLMs) to first generate a structured plan, followed by plan-driven simplification of individual sentences. At the document level, we leverage LLMs to produce concise summaries and subsequently guide the simplification process using these summaries. This two-stage, LLM-based framework enables more coherent and contextually faithful simplifications of scientific text.
@article{arxiv.2508.11816,
title = {LLM-Guided Planning and Summary-Based Scientific Text Simplification: DS@GT at CLEF 2025 SimpleText},
author = {Krishna Chaitanya Marturi and Heba H. Elwazzan},
journal= {arXiv preprint arXiv:2508.11816},
year = {2025}
}
Comments
Text Simplification, hallucination detection, LLMs, CLEF 2025, SimpleText, CEUR-WS