Topic-guided Example Selection for Domain Adaptation in LLM-based Machine Translation (2024.eacl-srw)
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| Challenge: | Current machine translation (MT) systems perform well in domains on which they were trained, but adaptation to unseen domains remains a challenge. |
| Approach: | They propose to use large language models to adapt to unseen domains by in-context example selection. |
| Outcome: | The proposed method outperforms baselines on multilingual out-of-domain tests, though it does not match performance with strong baselines for the in-language setting. |
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| Challenge: | Existing studies have shown that in-context examples for machine translation are beneficial for high-resource languages. |
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| Challenge: | Large-scale generative models can perform a wide range of NLP tasks using in-context learning. |
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| Challenge: | Recent studies focus on optimizing translation quality, with limited attention to understanding specific aspects of ICL that influence the said quality. |
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What do Large Language Models Need for Machine Translation Evaluation? (2024.emnlp-main)
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Shenbin Qian, Archchana Sindhujan, Minnie Kabra, Diptesh Kanojia, Constantin Orasan, Tharindu Ranasinghe, Fred Blain
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Exploring In-context Example Generation for Machine Translation (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have demonstrated strong performance across various tasks with just a few examples. |
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