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        <title>Dissecting In-Context Learning of Translations in GPT-3</title>
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        <namePart type="given">Vikas</namePart>
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    <abstract>Most of the recent work in leveraging Large Language Models (LLMs) such as GPT-3 for Machine Translation (MT) has focused on selecting the few-shot samples for prompting. In this work, we try to better understand the role of demonstration attributes for the in-context learning of translations through perturbations of high-quality, in-domain demonstrations. We find that asymmetric perturbation of the source-target mappings yield vastly different results. We show that the perturbation of the source side has surprisingly little impact, while target perturbation can drastically reduce translation quality, suggesting that it is the output text distribution that provides the most important learning signal during in-context learning of translations. We propose a method named Zero-Shot-Context to add this signal automatically in Zero-Shot prompting. We demonstrate that it improves upon the zero-shot translation performance of GPT-3, even making it competitive with few-shot prompted translations.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-emnlp.61</identifier>
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%0 Conference Proceedings
%T Dissecting In-Context Learning of Translations in GPT-3
%A Raunak, Vikas
%A Menezes, Arul
%A Awadalla, Hany
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F raunak-etal-2023-dissecting
%X Most of the recent work in leveraging Large Language Models (LLMs) such as GPT-3 for Machine Translation (MT) has focused on selecting the few-shot samples for prompting. In this work, we try to better understand the role of demonstration attributes for the in-context learning of translations through perturbations of high-quality, in-domain demonstrations. We find that asymmetric perturbation of the source-target mappings yield vastly different results. We show that the perturbation of the source side has surprisingly little impact, while target perturbation can drastically reduce translation quality, suggesting that it is the output text distribution that provides the most important learning signal during in-context learning of translations. We propose a method named Zero-Shot-Context to add this signal automatically in Zero-Shot prompting. We demonstrate that it improves upon the zero-shot translation performance of GPT-3, even making it competitive with few-shot prompted translations.
%R 10.18653/v1/2023.findings-emnlp.61
%U https://aclanthology.org/2023.findings-emnlp.61
%U https://doi.org/10.18653/v1/2023.findings-emnlp.61
%P 866-872

Markdown (Informal)

[Dissecting In-Context Learning of Translations in GPT-3](https://aclanthology.org/2023.findings-emnlp.61) (Raunak et al., Findings 2023)

• Dissecting In-Context Learning of Translations in GPT-3 (Raunak et al., Findings 2023)

ACL

• Vikas Raunak, Arul Menezes, and Hany Awadalla. 2023. Dissecting In-Context Learning of Translations in GPT-3. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 866–872, Singapore. Association for Computational Linguistics.