Can AI mimic the human ability to define neologisms?

An ongoing and intriguing debate focuses on whether Large Language Models (LLMs) can replicate human language. The literature presents mixed evidence on this matter. Several studies suggest that LLMs can generate text closely resembling human language (Bubeck et al., 2023; Clark et al., 2021; Georgiou, 2025). However, the widely accept ed concept of a universal grammar inherent in humans (Chomsky, 2000) challenges the idea that machine cognition can mirror human cognition. According to Chomsky et al. (2023), models like ChatGPT function as statistical engines driven by pattern recognitio n. Supporting this perspective, other studies highlight significant differences between human cognition and LLMs, which are reflected in language (Cai et al., 2024; Georgiou, 2024; Herbold et al., 2023). For instance, Georgiou (2024) examined how various linguistic components are represented in human - written and AI - generated texts, assessing the ability of ChatGPT to emulate human writing. The author found that d espite AI - generated texts appear ing to mimic human language, the results revealed signifi cant differences across multiple linguistic features in the domains of phonology, grammar, and semantics.