Unveiling the Influence of Amplifying Language-Specific Neurons
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866914160788373504 |
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| author | Rahmanisa, Inaya Andrylie, Lyzander Marciano Ihsani, Mahardika Krisna Wicaksono, Alfan Farizki Wibowo, Haryo Akbarianto Aji, Alham Fikri |
| author_facet | Rahmanisa, Inaya Andrylie, Lyzander Marciano Ihsani, Mahardika Krisna Wicaksono, Alfan Farizki Wibowo, Haryo Akbarianto Aji, Alham Fikri |
| contents | Language-specific neurons in LLMs that strongly correlate with individual languages have been shown to influence model behavior by deactivating them. However, their role in amplification remains underexplored. This work investigates the effect of amplifying language-specific neurons through interventions across 18 languages, including low-resource ones, using three models primarily trained in different languages. We compare amplification factors by their effectiveness in steering to the target language using a proposed Language Steering Shift (LSS) evaluation score, then evaluate it on downstream tasks: commonsense reasoning (XCOPA, XWinograd), knowledge (Include), and translation (FLORES). The optimal amplification factors effectively steer output toward nearly all tested languages. Intervention using this factor on downstream tasks improves self-language performance in some cases but generally degrades cross-language results. These findings highlight the effect of language-specific neurons in multilingual behavior, where amplification can be beneficial especially for low-resource languages, but provides limited advantage for cross-lingual transfer. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_22581 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unveiling the Influence of Amplifying Language-Specific Neurons Rahmanisa, Inaya Andrylie, Lyzander Marciano Ihsani, Mahardika Krisna Wicaksono, Alfan Farizki Wibowo, Haryo Akbarianto Aji, Alham Fikri Computation and Language Machine Learning Language-specific neurons in LLMs that strongly correlate with individual languages have been shown to influence model behavior by deactivating them. However, their role in amplification remains underexplored. This work investigates the effect of amplifying language-specific neurons through interventions across 18 languages, including low-resource ones, using three models primarily trained in different languages. We compare amplification factors by their effectiveness in steering to the target language using a proposed Language Steering Shift (LSS) evaluation score, then evaluate it on downstream tasks: commonsense reasoning (XCOPA, XWinograd), knowledge (Include), and translation (FLORES). The optimal amplification factors effectively steer output toward nearly all tested languages. Intervention using this factor on downstream tasks improves self-language performance in some cases but generally degrades cross-language results. These findings highlight the effect of language-specific neurons in multilingual behavior, where amplification can be beneficial especially for low-resource languages, but provides limited advantage for cross-lingual transfer. |
| title | Unveiling the Influence of Amplifying Language-Specific Neurons |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2507.22581 |