Exploring In-context Example Generation for Machine Translation - ACL Anthology
[Exploring In-context Example Generation for Machine Translation](https://aclanthology.org/2025.findings-acl.1362/) (Lee et al., Findings 2025) ACL materials are Copyright © 1963–2026 ACL; other materials are copyrighted by their respective copyright holders. Materials prior to 2016 here are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 International License. Permission is granted to make copies for the purposes of teaching and research. Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License. The ACL Anthology is managed and built by the ACL Anthology team of volunteers. Site last built on 07 January 2026 at 01:10 UTC with commit ed95914.
Abstract Large language models (LLMs) have demonstrated strong performance across various tasks, leveraging their exceptional in-context learning ability with only a few examples.Accordingly, the selection of optimal in-context examples has been actively studied in the field of machine translation.However, these studies presuppose the presence of a demonstration pool with human-annotated pairs, making them less applicable to low-resource languages where such an assumption is challenging to meet.To overcome this limitation, this paper explores the research direction of in-context example…
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