Zero-shot LLMs with a new prompting method outperform prior unsupervised approaches on 13 of 14 readability datasets, and the hybrid LAURAE method improves robustness across languages, lengths, and technical content.
InProceedings of the 11th Workshop on Innovative Use of NLP for Building Educational Applications, pages 12–22
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Zero-shot Large Language Models for Automatic Readability Assessment
Zero-shot LLMs with a new prompting method outperform prior unsupervised approaches on 13 of 14 readability datasets, and the hybrid LAURAE method improves robustness across languages, lengths, and technical content.