In search of a translator: using AI to evaluate what's lost in translation - Elkins, Katherine (Frontiers in Computer Science, 2024)
Machine translation metrics often fall short in capturing the challenges of literary translation in which translators play a creative role. Large Language Models (LLMs) like GPT4o and Mistral offer new approaches to assessing how well a translation mirrors the reading experience from one language to another. Our case study focuses on the first volume of Marcel Proust's “A la recherche du temps perdu,” a work known for its lively translation debates. We use stylometry and emotional arc leveraging the newest multilingual generative AI models to evaluate loss in translation according to different translation theories. AI analysis reveals previously undertheorized aspects of translation. Notably, we uncover changes in authorial style and the evolution of sentiment language over time. Our study demonstrates that AI-driven approaches leveraging advanced LLMs yield new perspectives on literary translation assessment. These methods offer insight into the creative choices made by translators and open up new avenues for understanding the complexities of translating literary works.