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        <title>Self-Augmented In-Context Learning for Unsupervised Word Translation</title>
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        <namePart type="given">Yaoyiran</namePart>
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    <name type="personal">
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            <namePart type="given">Lun-Wei</namePart>
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    <abstract>Recent work has shown that, while large language models (LLMs) demonstrate strong word translation or bilingual lexicon induction (BLI) capabilities in few-shot setups, they still cannot match the performance of ‘traditional’ mapping-based approaches in the unsupervised scenario where no seed translation pairs are available, especially for lower-resource languages. To address this challenge with LLMs, we propose self-augmented in-context learning (SAIL) for unsupervised BLI: starting from a zero-shot prompt, SAIL iteratively induces a set of high-confidence word translation pairs for in-context learning (ICL) from an LLM, which it then reapplies to the same LLM in the ICL fashion. Our method shows substantial gains over zero-shot prompting of LLMs on two established BLI benchmarks spanning a wide range of language pairs, also outperforming mapping-based baselines across the board. In addition to achieving state-of-the-art unsupervised BLI performance, we also conduct comprehensive analyses on SAIL and discuss its limitations.</abstract>
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%0 Conference Proceedings
%T Self-Augmented In-Context Learning for Unsupervised Word Translation
%A Li, Yaoyiran
%A Korhonen, Anna
%A Vulić, Ivan
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F li-etal-2024-self-augmented
%X Recent work has shown that, while large language models (LLMs) demonstrate strong word translation or bilingual lexicon induction (BLI) capabilities in few-shot setups, they still cannot match the performance of ‘traditional’ mapping-based approaches in the unsupervised scenario where no seed translation pairs are available, especially for lower-resource languages. To address this challenge with LLMs, we propose self-augmented in-context learning (SAIL) for unsupervised BLI: starting from a zero-shot prompt, SAIL iteratively induces a set of high-confidence word translation pairs for in-context learning (ICL) from an LLM, which it then reapplies to the same LLM in the ICL fashion. Our method shows substantial gains over zero-shot prompting of LLMs on two established BLI benchmarks spanning a wide range of language pairs, also outperforming mapping-based baselines across the board. In addition to achieving state-of-the-art unsupervised BLI performance, we also conduct comprehensive analyses on SAIL and discuss its limitations.
%R 10.18653/v1/2024.acl-short.67
%U https://aclanthology.org/2024.luhme-short.67/
%U https://doi.org/10.18653/v1/2024.acl-short.67
%P 743-753

Markdown (Informal)

[Self-Augmented In-Context Learning for Unsupervised Word Translation](https://aclanthology.org/2024.luhme-short.67/) (Li et al., ACL 2024)

• Self-Augmented In-Context Learning for Unsupervised Word Translation (Li et al., ACL 2024)

ACL

• Yaoyiran Li, Anna Korhonen, and Ivan Vulić. 2024. Self-Augmented In-Context Learning for Unsupervised Word Translation. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 743–753, Bangkok, Thailand. Association for Computational Linguistics.