比较不同提示策略，发现先翻译再简化最有效。
Translate or Simplify First: An Analysis of Cross-lingual Text Simplification in English and French
- 分步处理：先翻译后简化，效果优于同步进行。
- 先译后简在语言简洁性上表现最佳。
- 适合需要多语言可读性的内容优化场景。
跨语言文本简化（CLTS）旨在通过同时处理语言复杂性和翻译问题，提升内容在不同语言间的可访问性。本研究探讨了使用大语言模型（LLMs）在英法语之间进行CLTS时不同提示策略的有效性。我们评估了五种提示系统：直接提示（同时翻译与简化）、两种组合方法（先译后简或先简后译）以及两种分解方法（分步执行翻译与简化）。实验覆盖五个不同语料库（包括维基百科和医学文本），采用七种前沿大模型进行测试。输出质量通过自动指标、全面的语言特征分析及人类对简洁性与语义保留的评估进行多维度评价。结果表明，尽管直接提示在BLEU得分上最高（体现语义保真度），但‘先翻译再简化’策略在语言简洁性方面表现最优。
原文摘要 · Abstract (English)
Cross-Lingual Text Simplification (CLTS) aims to make content more accessible across languages by simultaneously addressing both linguistic complexity and translation. This study investigates the effectiveness of different prompting strategies for CLTS between English and French using large language models (LLMs). We examine five distinct prompting systems: a direct prompt instructing the LLM to perform both translation and simplification simultaneously, two Composition approaches that either translate-then-simplify or simplify-then-translate within a single prompt, and two decomposition approaches that perform the same operations in separate, consecutive prompts. These systems are evaluated across a diverse set of five corpora of different genres (Wikipedia and medical texts) using seven state-of-the-art LLMs. Output quality is assessed through a multi-faceted evaluation framework comprising automatic metrics, comprehensive linguistic feature analysis, and human evaluation of simplicity and meaning preservation. Our findings reveal that while direct prompting consistently achieves the highest BLEU scores, indicating meaning fidelity, Translate-then-Simplify approaches demonstrate the highest simplicity, as measured by the linguistic features.
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