发表机构
SCE; ScaDS.AI, TUD Dresden（SCE（以色列SCE学院）; ScaDS.AI，德累斯顿工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对文档级文本简化中提示指导不足的问题，提出示例引导提示方法，经OneStopEnglish语料库实验，可提升大语言模型的简化质量，性能优于或媲美相关基准系统。
AI 中文摘要
文档级文本简化要求大语言模型（LLMs）重写复杂文档，同时保留原意、可读性和语篇连贯性。尽管基于提示的LLMs已展现出良好性能，但它们常生成不一致的简化结果，因为仅文本指令对复杂的文档级转换提供的指导有限。我们研究从并行简化语料库中选取的检索到的文档简化示例，能否通过用这些示例增强提示来改进文档级生成。这种示例引导提示方法使LLMs无需特定任务微调即可利用相关简化模式。在OneStopEnglish语料库上使用多个最先进的LLMs进行的实验表明，与仅用提示的生成相比，纳入检索示例可持续提升简化质量，且与代表性的监督式（T5）和基于规划的（PlanSimp）文档简化系统相比，性能相当或更优。此外，我们发现示例引导提示的益处因LLMs而异，这表明有效利用检索示例取决于模型在生成过程中整合上下文信息的能力。
英文摘要
Document-level text simplification requires large language models (LLMs) to rewrite complex documents while preserving meaning, readability, and discourse coherence. Although prompt-based LLMs have shown promising performance, they often produce inconsistent simplifications because textual instructions alone provide limited guidance for complex document-level transformations. We investigate whether retrieved document-simplification examples can improve document-level generation by augmenting prompts with examples selected from a parallel simplification corpus. This example-guided prompting approach enables LLMs to exploit relevant simplification patterns without task-specific fine-tuning. Experiments on the OneStopEnglish corpus using multiple state-of-the-art LLMs show that incorporating retrieved examples consistently improves simplification quality over prompt-only generation and achieves competitive or superior performance compared with representative supervised (T5) and planning-based (PlanSimp) document simplification systems. Furthermore, we find that the benefits of example-guided prompting vary across LLMs, suggesting that effective use of retrieved examples depends on a model's ability to integrate contextual information during generation.
