One of the common errors which translators commit when transferring terms from one lan- guage into another is erroneously coining terms which are based on a false friend mistake due to the similarity between lexical units forming part of terms. In this case-study, we use Chat- GPT to automatically detect terms in Spanish which may be coined based on a false friend relation. To carry out this study, we imple- mented two experiments with GPT and com- pared the results. In the first, we prompted GPT to produce a list of twenty terms in Span- ish extracted from the UN discourse, which are possibly based on false friend relation, and its English equivalents and analysed the veracity of the results. In the second experiment, we used an aligned corpus to further study the ca- pabilities of the Language Model on detecting false friends in English and Spanish Text. Some results were significant for future terminologi- cal studies.

Multiword Terms (MWTs) are domain-specific Multiword Expressions (MWE) where two or more lexemes converge to form a new unit of meaning. The task of processing MWTs is crucial in many Natural Language Processing (NLP) applications, including Machine Translation (MT) and terminology extraction. However, the automatic detection of those terms is a difficult task and more research is still required to give more insightful and useful results in this field. In this study, we seek to fill this gap using state-of-the-art transformer models. We evaluate both BERT like discriminative transformer models and generative pre-trained transformer (GPT) models on this task, and we show that discriminative models perform better than current GPT models in multi-word terms identification task in flower and plant names in English and Spanish languages. Best discriminate models perform 94.3127%, 82.1733% F1 scores in English and Spanish data, respectively while ChatGPT could only perform 63.3183% and 47.7925% respectively.