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 2023 Conference on Empirical Methods in Natural Language Processing, pages 5506–5524, Singapore
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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.