用大模型生成计划与摘要，让科学文本简化更连贯准确
LLM-Guided Planning and Summary-Based Scientific Text Simplification: DS@GT at CLEF 2025 SimpleText
- 先让大模型生成结构化简化计划，再逐句执行
- 文档级简化依赖大模型生成的摘要来指导内容重组
- 适合需要精准简化科研文献的研究者和科普作者
本文介绍了我们在 CLEF 2025 SimpleText 任务1中的方法，针对句子级和文档级科学文本简化问题。对于句子级简化，采用大语言模型（LLMs）首先生成结构化计划，再基于该计划进行逐句简化；在文档级层面，利用大语言模型生成简洁摘要，并以此摘要为引导开展文本简化。这种两阶段、基于大模型的框架，实现了更连贯且忠实于原文语境的科学文本简化。
原文摘要 · Abstract (English)
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.
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