TopXGen: Topic-Diverse Parallel Data Generation for Low-Resource Machine Translation (2025.findings-emnlp)
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| Challenge: | In-context learning and similarity search have been shown to improve LLMs' performance in machine translation, but they lag behind when dealing with low-resource languages. |
| Approach: | They propose a method that uses an LLM to generate topic-specific target-side data in the LRL. |
| Outcome: | The proposed approach boosts LLM translation performance during in-context learning and fine-tuning. |
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