CRANE: Causal Relevance Analysis of Language-Specific Neurons in Multilingual Large Language Models

• URL: http://arxiv.org/abs/2601.04664v1
• Date: Thu, 08 Jan 2026 07:21:13 GMT
• Title: CRANE: Causal Relevance Analysis of Language-Specific Neurons in Multilingual Large Language Models
• Authors: Yifan Le, Yunliang Li,
• Abstract summary: How language capabilities are organized at the neuron level remains poorly understood.<n>We propose CRANE, a relevance-based analysis framework that redefines language specificity in terms of functional necessity.
• Score: 0.021485350418225243
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multilingual large language models (LLMs) achieve strong performance across languages, yet how language capabilities are organized at the neuron level remains poorly understood. Prior work has identified language-related neurons mainly through activation-based heuristics, which conflate language preference with functional importance. Prior work has identified language-related neurons mainly through activation-based heuristics, which conflate language preference with functional importance. We propose CRANE, a relevance-based analysis framework that redefines language specificity in terms of functional necessity, identifying language-specific neurons through targeted neuron-level interventions. CRANE characterizes neuron specialization by their contribution to language-conditioned predictions rather than activation magnitude. Our implementation will be made publicly available. Neuron-level interventions reveal a consistent asymmetric pattern: masking neurons relevant to a target language selectively degrades performance on that language while preserving performance on other languages to a substantial extent, indicating language-selective but non-exclusive neuron specializations. Experiments on English, Chinese, and Vietnamese across multiple benchmarks, together with a dedicated relevance-based metric and base-to-chat model transfer analysis, show that CRANE isolates language-specific components more precisely than activation-based methods.

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