Large Language Models (LLMs) such as GPT-3 have emerged as general-purpose
language models capable of addressing many natural language generation or
understanding tasks. On the task of Machine Translation (MT), multiple works
have investigated few-shot prompting mechanisms to elicit better translations
from LLMs. However, there has been relatively little investigation on how such
translations differ qualitatively from the translations generated by standard
Neural Machine Translation (NMT) models. In this work, we investigate these
differences in terms of the literalness of translations produced by the two
systems. Using literalness measures involving word alignment and monotonicity,
we find that translations out of English (E-X) from GPTs tend to be less
literal, while exhibiting similar or better scores on MT quality metrics. We
demonstrate that this finding is borne out in human evaluations as well. We
then show that these differences are especially pronounced when translating
sentences that contain idiomatic expressions.

本文研究大型语言模型（LLMs）在机器翻译（MT）中的应用，发现使用 GPT-3 等模型的翻译虽然在质量上相当或更好，但在语句的逐字逐句的程度上比标准 NMT 模型的翻译要少。此外，当翻译的句子中包含习语表达时，这种差异尤为明显。