Age of acquisition (AoA) is a widely used variable that estimates when a lexical item is first understood. Existing English AoA norms have been highly influential in psycholinguistics, education, language acquisition, speech-language pathology, and natural language processing, but have focused primarily on single words. Little information is availed for multi-word expressions (MWEs), despite their central role in language use, vocabulary acquisition, representation and processing. The current study contributes AoA estimates for 80,586 English MWEs using a large language model, GPT-4.1-mini, fine-tuned on newly collected crowdsourced human ratings. Ratings were obtained from 96 US-based native English speakers via Prolific, yielding 47,163 ratings for ~3,999 MWEs. After reliability screening, 3,667 BLUP-adjusted means were used for LLM fine-tuning and validation. Fine-tuning substantially improved alignment with hold-out human ratings. The standard GPT-4.1-mini output correlated with human estimates at r =.67, whereas the model fine-tuned on 3,000 items reached r =.85. A final model trained on all reliable crowdsourced estimates was estimated AoAs for the full MWE list. Results showed relationships with existing psycholinguistic variables aligned with those for single-word AoAs, including that earlier-acquired MWEs tended to be more familiar, useful, and frequent. The estimates exhibited predictive validity against test-based student vocabulary data and explained additional variance beyond frequency, utility, and familiarity. These findings indicate that fine-tuned LLMs can provide useful large-scale AoA estimates for MWEs when grounded in human ratings. The new resource is available via OSF (https://tinyurl.com/3e828fj8) and an interactive webpage has been developed for users: https://cgg-projects.github.io/MWEs/.
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