Abstract
Large language models (LLMs) enable strong text generation, and in general there is a practical tradeoff between fine-tuning and prompt engineering. We introduce Simplify-This, a comparative study evaluating both paradigms for text simplification with encoder-decoder LLMs across multiple benchmarks, using a range of evaluation metrics. Fine-tuned models consistently deliver stronger structural simplification, whereas prompting often attains higher semantic similarity scores yet tends to copy inputs. A human evaluation favors fine-tuned outputs overall. We release code, a cleaned derivative dataset used in our study, checkpoints of fine-tuned models, and prompt templates to facilitate reproducibility and future work.
Abstract (translated)
大型语言模型（LLMs）能够实现强大的文本生成，在一般情况下，微调和提示工程之间存在实际的权衡。我们介绍了一项名为“Simplify-This”的比较研究，该研究评估了使用编码器-解码器LLMs在多个基准测试中针对文本简化进行微调与提示这两种方法的效果，并采用了多种评价指标。微调模型始终能提供更强的结构化简化效果，而通过提示往往能达到更高的语义相似度得分，但却倾向于复制输入内容。从人类评估的角度来看，总体上更偏好微调后的输出结果。我们发布了代码、用于研究的清理过的衍生数据集、微调模型的检查点以及提示模板以促进可重复性和未来的研究工作。
URL
https://arxiv.org/abs/2601.05794
