PLUG: Leveraging Pivot Language in Cross-Lingual Instruction Tuning (2024.acl-long)
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| Challenge: | Instruction tuning has advanced large language models (LLMs) but its application in lower-resource languages faces challenges due to the imbalanced foundational abilities of LLMs across different languages. |
| Approach: | They propose a pivot language guided generation approach that utilizes a high-resource language as the pivot to enhance instruction tuning in lower-resourced languages. |
| Outcome: | The proposed approach improves instruction-following abilities of LLMs by 29% on average compared to directly responding in the target language alone. |
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Alexander Weber, Klaudia Thellmann, Jan Ebert, Nicolas Flores-Herr, Jens Lehmann, Michael Fromm, Mehdi Ali
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| Challenge: | Prior work on multilingual evaluation has shown that there is a large gap between the performance of Large Language Models on English and other languages. |
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