Large Language Models (LLMs) have demonstrated impressive performance in translating content across different languages and genres. Yet, their potential in the creative aspects of machine translation has not been fully explored. In this paper, we seek to identify the strengths and weaknesses inherent in different LLMs when applied to one of the most prominent features of creative works: the translation of idiomatic expressions. We present an overview of their performance in the EN→IT language pair, a context characterized by an evident lack of bilingual data tailored for idiomatic translation. Lastly, we investigate the impact of prompt design on the quality of machine translation, drawing on recent findings which indicat a substantial variation in the performance of LLMs depending on the prompts utilized.
Prompting Large Language Models for Idiomatic Translation
Castaldo Antonio
Writing – Original Draft Preparation
;Monti Johanna
Writing – Review & Editing
2024-01-01
Abstract
Large Language Models (LLMs) have demonstrated impressive performance in translating content across different languages and genres. Yet, their potential in the creative aspects of machine translation has not been fully explored. In this paper, we seek to identify the strengths and weaknesses inherent in different LLMs when applied to one of the most prominent features of creative works: the translation of idiomatic expressions. We present an overview of their performance in the EN→IT language pair, a context characterized by an evident lack of bilingual data tailored for idiomatic translation. Lastly, we investigate the impact of prompt design on the quality of machine translation, drawing on recent findings which indicat a substantial variation in the performance of LLMs depending on the prompts utilized.File | Dimensione | Formato | |
---|---|---|---|
CTT_2024_proceedings.pdf
accesso aperto
Tipologia:
Documento in Post-print
Licenza:
DRM non definito
Dimensione
7.48 MB
Formato
Adobe PDF
|
7.48 MB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.