Unveiling the Influence of Amplifying Language-Specific Neurons
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 15 of its 28 references that are in the map. First posted on arXiv 30 Jul 2025.
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15 of 28 references.
References · 15
- 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,935
- 2022Locating and Editing Factual Associations in GPTcited by 2,691
- 2024Gemma 2: Improving Open Language Models at a Practical Sizecited by 1,998
- 2023Towards Best Practices of Activation Patching in Language Models: Metrics and Methodscited by 359
- 2020XCOPA: A Multilingual Dataset for Causal Commonsense Reasoningcited by 333
- 2024How do Large Language Models Handle Multilingualism?cited by 150
- 2021Multilingual LAMA: Investigating Knowledge in Multilingual Pretrained Language Modelscited by 132
- 2024Language-Specific Neurons: The Key to Multilingual Capabilities in Large Language Modelscited by 126
- 2024On the Multilingual Ability of Decoder-based Pre-trained Language Models: Finding and Controlling Language-Specific Neuronscited by 83
- 2024Explainable and Interpretable Multimodal Large Language Models: A Comprehensive Surveycited by 52
- 2024SeaLLMs 3: Open Foundation and Chat Multilingual Large Language Models for Southeast Asian Languagescited by 36
- 2024Sharing Matters: Analysing Neurons Across Languages and Tasks in LLMscited by 33
3 more
- 2019Effective Cross-lingual Transfer of Neural Machine Translation Models without Shared Vocabulariescited by 27
- 2024Towards Understanding Multi-Task Learning (Generalization) of LLMs via Detecting and Exploring Task-Specific Neuronscited by 25
- 2025Language-specific Neurons Do Not Facilitate Cross-Lingual Transfercited by 11
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Citing papers per year: 2026: 3.
3 papers.
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- 2025The Transfer Neurons Hypothesis: An Underlying Mechanism for Language Latent Space Transitions in Multilingual LLMs
- 2024Probing the Emergence of Cross-lingual Alignment during LLM Training
- 2025What Language(s) Does Aya-23 Think In? How Multilinguality Affects Internal Language Representations
- 2024Converging to a Lingua Franca: Evolution of Linguistic Regions and Semantics Alignment in Multilingual Large Language Models
- 2024The LLM Language Network: A Neuroscientific Approach for Identifying Causally Task-Relevant Units