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A Meta-evaluation of Automatic Metrics for Elaborative Simplification

Elaborative simplification aims to improve the readability of texts by adding content that helps the readers. However, evaluating these elaborations remains challenging due to their subjective nature and the lack of suitable annotated datasets. To support the evaluation of elaborative simplification models, we introduce a new dataset with human ratings of elaborations generated by Large Language Models (LLMs), focusing on two quality criteria: cohesion and informativeness. Using these human judgments as a reference, we conduct a meta-evaluation of existing automatic evaluation approaches, with a focus on LLM-as-a-judge strategies. Our experiments suggest that evaluations made by smaller LLMs correlate poorly with human judgments, while larger models with structured prompting exhibit higher agreement. Informativeness evaluation proved to be challenging due to its subjectivity, as evidenced by the low inter-annotator agreement compared to cohesion.