New prompting method enhances LLM document simplification with examples

Researchers have developed an example-guided prompting approach to improve document-level text simplification using large language models (LLMs). This method augments standard prompts with relevant simplification examples retrieved from a parallel corpus, enabling LLMs to learn simplification patterns without task-specific fine-tuning. Experiments on the OneStopEnglish corpus demonstrated that this technique consistently enhances simplification quality compared to prompt-only generation and rivals supervised and planning-based systems. The effectiveness of example-guided prompting was found to vary across different LLMs, indicating that a model's capacity to integrate contextual information influences its ability to leverage retrieved examples. AI

IMPACT This approach could improve the quality and consistency of text simplification tasks performed by LLMs, making complex documents more accessible.

RANK_REASON This is a research paper detailing a new method for text simplification using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• arXiv
• Hugging Face
• OneStopEnglish corpus
• PlanSimp
• T5 Text To Text Transfer Transformer

• paper
• model release

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