How does Alignment Enhance LLMs' Multilingual Capabilities? A Language Neurons Perspective

2025-05-27

AI Summary

How multilingual alignment enhances large language models’ (LLMs) multilingual capabilities remains poorly understood at the neural level. Method: We propose a fine-grained neuron identification algorithm to jointly detect language-specific and language-invariant neurons; construct a four-stage, neuron-distribution-based multilingual reasoning mechanism—comprising comprehension, semantic reasoning, output-space transformation, and vocabulary generation; and empirically investigate “spontaneous multilingual alignment,” i.e., cross-lingual neuronal response coupling in unaligned models. Using activation analysis, neuron importance scoring, attention visualization, and cross-lingual statistical modeling, we quantify alignment effects on low-resource language representations. Contribution/Results: Alignment significantly improves activation specificity of low-resource language–associated neurons; the four-stage mechanism is validated by neural activity patterns; and we establish the first interpretable, neuron-level mechanistic model of multilingual reasoning while discovering that alignment exhibits intrinsic emergent properties—even without explicit training objectives.

Technology Category

• Natural Language Processing: Language Grounding & Multi-modal NLP
• Machine Learning: Large Multimodal Models (LMMs)
• Knowledge Representation and Reasoning: Knowledge Representation Languages

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Multilingual Alignment is an effective and representative paradigm to enhance LLMs' multilingual capabilities, which transfers the capabilities from the high-resource languages to the low-resource languages. Meanwhile, some researches on language-specific neurons reveal that there are language-specific neurons that are selectively activated in LLMs when processing different languages. This provides a new perspective to analyze and understand LLMs' mechanisms more specifically in multilingual scenarios. In this work, we propose a new finer-grained neuron identification algorithm, which detects language neurons~(including language-specific neurons and language-related neurons) and language-agnostic neurons. Furthermore, based on the distributional characteristics of different types of neurons, we divide the LLMs' internal process for multilingual inference into four parts: (1) multilingual understanding, (2) shared semantic space reasoning, (3) multilingual output space transformation, and (4) vocabulary space outputting. Additionally, we systematically analyze the models before and after alignment with a focus on different types of neurons. We also analyze the phenomenon of ''Spontaneous Multilingual Alignment''. Overall, our work conducts a comprehensive investigation based on different types of neurons, providing empirical results and valuable insights for better understanding multilingual alignment and multilingual capabilities of LLMs.

Problem

• How alignment improves multilingual capabilities in LLMs
• Identifying language-specific and language-agnostic neurons in LLMs
• Analyzing multilingual inference processes before and after alignment

Innovation

• Finer-grained neuron identification algorithm
• Divides LLMs' process into four parts
• Analyzes alignment impact on neuron types