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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.
GPT 比传统机器翻译更加深刻
多项研究发现少量提示可以从大型语言模型中引发更好的翻译，但是关于此类翻译与标准神经机器翻译 (NMT) 模型生成的翻译，在质量上有何不同的研究相对较少。
在此项研究中，微软 Azure AI 团队使用了涉及单词对齐和单调性的字面量度，发现 GPT 的英语 (E-X) 翻译往往较少字面直译，同时在机器翻译质量指标上表现出相似或更好的分数，这些差异在翻译惯用语句时尤其明显，这一发现在人类评估中也得到了证实。

www.tgoop.com/ssr58/407

1.3K viewsDr. Morse, May 30, 2023 at 03:41

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.
GPT 比传统机器翻译更加深刻
多项研究发现少量提示可以从大型语言模型中引发更好的翻译，但是关于此类翻译与标准神经机器翻译 (NMT) 模型生成的翻译，在质量上有何不同的研究相对较少。
在此项研究中，微软 Azure AI 团队使用了涉及单词对齐和单调性的字面量度，发现 GPT 的英语 (E-X) 翻译往往较少字面直译，同时在机器翻译质量指标上表现出相似或更好的分数，这些差异在翻译惯用语句时尤其明显，这一发现在人类评估中也得到了证实。

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