Unveiling the Influence of Amplifying Language-Specific Neurons

2025-07-30

AI Summary

This work investigates the impact of language-specific neuron amplification on multilingual large language models (MLLMs), focusing on its mechanisms in low-resource language enhancement and cross-lingual transfer. We propose the Language-Specificity Shift (LSS) metric—a neuron-level, intervention-based interpretability measure—and systematically evaluate commonsense reasoning, knowledge understanding, and translation across 18 languages and three training paradigms. Results show that moderate amplification of language-specific neurons significantly improves target-language generation accuracy and monolingual performance for low-resource languages, yet consistently degrades cross-lingual generalization. Our key contributions are threefold: (1) the first quantitative characterization of the “gain–cost” trade-off inherent in language-neuron amplification; (2) the introduction of LSS as a novel, interpretable metric for assessing language specificity in neural representations; and (3) theoretical insights and practical guidelines for language disentanglement and controllable linguistic steering in MLLMs.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Computer Vision: Language and Vision

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

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.

Problem

• Investigates amplifying language-specific neurons in multilingual LLMs
• Evaluates impact on language steering and downstream tasks
• Explores benefits for low-resource languages versus cross-lingual transfer

Innovation

• Amplifying language-specific neurons in LLMs
• Evaluating with Language Steering Shift score
• Improving low-resource language performance