Drafting Definitions for Emerging Concepts and Terms Undergoing Semantic Shift Within the ARTES Knowledge Base: A Protocol for Integrating LLMS Into Terminological Analysis by Experimental Approach

Abstract

This paper presents a protocol for evaluating and integrating generative AI (GenAI) tools in the framework of the terminological analysis of emerging, semantically unstable terms, absent from established term bases. Implemented within the ARTES knowledge base, the protocol supports Master’s students in translation at Université Paris Cité, in their task consisting of conducting a terminological analysis required for their dissertation. The study focuses particularly on terms displaying semantic instability and variation, thereby giving rise to semantic neologisms, and on evaluating the effectiveness of GenAI in retrieving existing definitions and drafting new terminological definitions for such terms. A survey of students’ GenAI use and an experimental study on the concept of data pollution illustrate the approach. Findings show corpus-linguistic tools help structure conceptual knowledge and critically assess GenAI outputs, confirming the need for human oversight. The study proposes a model for prompt construction and evaluation, a systematic process for building a collection of effective prompts, and a methodology that combines LLMs and corpus linguistics’ techniques for terminology management.