Abstract
Abstract This article explores the necessary transformation of terminology theory and practice induced by advances in artificial intelligence (AI), in particular generative AI and large language models (LLMs). It underlines the centrality of terminology for accurate and efficient multilingual communication, and it studies the interaction between two different forms of language, the embodied language of humans and the algorithmic and mathematical language of AI. The analysis traces the evolution of terminology, from its normative, concept-centric origins to contemporary challenges such as the concept-context gap, multilingual conceptual modeling and enriching terminologies with contextual relations. The article then highlights how AI and LLM are revolutionizing terminology work, by proposing the construction of relations between terms around prototypical contexts. This innovative approach aims to infuse a more deterministic understanding into human-AI interactions, guiding the latter to generate responses more aligned with human intent and context. Advocating a re-evaluation of current methodologies to interact better with AI's algorithmic mathematical interpretation of natural language, the article envisions a future in which terminology science and AI co-create a framework for improving multilingual knowledge exchange and understanding.
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