Measuring Creative Phraseology in Literature: Machine Translation Systems Versus Large Language Models

In a growing digital scenario where phraseology has become aware of technological realities, literary translation is timidly testing sophisticated AI-based tools. This study aims at assessing the quality of the output rendered by neural machine translation (NMT) systems, i.e., DeepL and Google Translate, and large language models (LLMs), i.e., ChatGPT and Gemini, in the English>Spanish translation of five comparative idioms extracted from literary texts. To this end, professional literary translators and translation undergraduates evaluate their output against human translation (HT), following the parameters proposed by Corpas Pastor and Noriega-Santiáñez (2024) to measure creativity in the translation of multiword-expressions: adequacy (morphosyntactic, semantic, and pragmatic) and novelty. The findings show that HT stands out, although NMT systems outperformed morphosyntactically. LLMs, especially ChatGTP, show promising creative results. Therefore, this study serves to reflect on the use of technologies for the translation of creative phraseology in the context of literature.