Multilingual Emotion Neurons in Large Audio-Language Models

• URL: http://arxiv.org/abs/2608.08772v1
• Date: Sun, 09 Aug 2026 15:42:55 GMT
• Title: Multilingual Emotion Neurons in Large Audio-Language Models
• Abstract summary: Emotion is central to human communication, and its expression varies across languages.<n>It remains unclear whether large audio-language models (LALMs) encode emotion through language-specific correlations or language-agnostic representations.<n>We present the first neuron-level interpretability study of this question.
• Score: 15.987865011030387
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Emotion is central to human communication, and its expression varies across languages. Large audio-language models (LALMs) achieve strong performance on multilingual speech tasks, yet it remains unclear whether they encode emotion through language-specific correlations or language-agnostic representations. We present the first neuron-level interpretability study of this question. We define Multilingual Emotion Neurons (MLENs) as functional units exhibiting stable emotional selectivity and aligned causal effects across languages, and introduce Consistency-Regularized Fusion (CR-Fusion) to identify them. Across four modern LALMs and 12 typologically diverse languages, emotion-sensitive neurons identified independently per language show minimal overlap, and additional monolingual identification data saturates quickly without isolating more transferable units, motivating identification from pooled cross-lingual evidence. Causal interventions demonstrate that MLENs identified by CR-Fusion provide more precise and transferable affective control than monolingual neuron sets in both zero-shot and low-resource settings. Leave-one-out ablations further reveal asymmetric transfer: individual identification languages, including low-resource ones, contribute non-redundant evidence, while several low-resource languages benefit most from the resulting cross-lingual transfer. Together, our findings provide the first causal, neuron-level account of how LALMs encode emotion across languages, and establish multilingual neuron identification as an effective mechanism for understanding cross-lingual affective behavior.

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