Problem
Research questions and friction points this paper is trying to address.
document-level text simplification
large language models
prompting
discourse coherence
simplification consistency
Example-Guided Prompting for Document-Level Text Simplification
🤖 AI Summary
Large language models (LLMs) often struggle to simultaneously preserve semantic content, ensure readability, and maintain discourse coherence in document-level text simplification when relying solely on instruction-based prompting. To address this limitation, this work proposes a retrieval-augmented, exemplar-guided prompting approach that dynamically retrieves relevant examples from a parallel simplification corpus and incorporates them into the prompt, thereby guiding the model to produce more consistent and higher-quality simplified texts without requiring task-specific fine-tuning. Evaluated systematically on the OneStopEnglish corpus, the proposed method significantly outperforms pure prompting baselines and matches or exceeds the performance of supervised and planning-based systems such as T5 and PlanSimp. Furthermore, this study provides the first empirical analysis of the varying capacities among different LLMs to effectively leverage retrieved exemplars for simplification.
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
example-guided prompting
document-level text simplification
retrieval-augmented generation
large language models
prompt engineering
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