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There is growing interest in systems that generate captions for scientificfigures. However, assessing these systems output poses a significant challenge.Human evaluation requires academic expertise and is costly, while automaticevaluation depends on often low-quality author-written captions. This paperinvestigates using large language models (LLMs) as a cost-effective,reference-free method for evaluating figure captions. We first constructedSCICAP-EVAL, a human evaluation dataset that contains human judgments for 3,600scientific figure captions, both original and machine-made, for 600 arXivfigures. We then prompted LLMs like GPT-4 and GPT-3 to score (1-6) each captionbased on its potential to aid reader understanding, given relevant context suchas figure-mentioning paragraphs. Results show that GPT-4, used as a zero-shotevaluator, outperformed all other models and even surpassed assessments made byComputer Science and Informatics undergraduates, achieving a Kendallcorrelation score of 0.401 with Ph.D. students rankings

GPT-4 as an Effective Zero-Shot Evaluator for Scientific Figure Captions