Understanding In-Context Machine Translation for Low-Resource Languages: A Case Study on Manchu (2025.acl-long)
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| Challenge: | In-context machine translation (MT) with large language models can take advantage of linguistic resources such as grammar books and dictionaries. |
| Approach: | They propose to use in-context machine translation (MT) with large language models to take advantage of linguistic resources such as grammar books and dictionaries. |
| Outcome: | The proposed approach can take advantage of dictionaries and grammar books, but its performance is poor for many lowresource languages. |
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