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Language ability at an old age is a balance between preservation and decline. Modelling baseline language variation in normal aging thus is important for our understanding of healthy aging, which can help detect cognitive impairments at the prodromal stage. Large-language databases and NLP tools enable us to conduct automated quantitative analysis of natural language data. In this study, we aim to demonstrate that (i) age and sex influence old adults’ lexical distribution and lexical concreteness; and (ii) using NLP tools and psycholinguistic metrics to process natural language datasets can help to set a normative benchmark of aging languages.