Sparse Subnetwork Enhancement for Underrepresented Languages in Large Language Models

• URL: http://arxiv.org/abs/2510.13580v1
• Date: Wed, 15 Oct 2025 14:14:49 GMT
• Title: Sparse Subnetwork Enhancement for Underrepresented Languages in Large Language Models
• Authors: Daniil Gurgurov, Josef van Genabith, Simon Ostermann,
• Abstract summary: Large language models exhibit uneven performance across languages.<n>We present a framework for enhancing monolingual capabilities of LLMs in underrepresented languages.<n>Our approach identifies language-specific neurons using Language Activation Probability Entropy and fine-tunes only the weights associated with these neurons.
• Score: 11.719190735841407
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models exhibit uneven performance across languages, with substantial gaps between high- and low-resource languages. We present a framework for enhancing monolingual capabilities of LLMs in underrepresented languages while preserving their general-purpose performance through targeted fine-tuning of language-specific subnetworks. Our approach identifies language-specific neurons using Language Activation Probability Entropy and fine-tunes only the weights associated with these neurons, a dedicated subnetwork, on target-language data. Experiments on Llama-3.1-8B and Mistral-Nemo-12B across 12 mid- and low-resource languages demonstrate that our method consistently outperforms full fine-tuning, FFN-only fine-tuning, LoRA adaptation, and random subset fine-tuning baselines while efficiently updating only up to 1% of model parameters. Beyond performance improvements, we observe enhanced favorable training dynamics, cross-lingual representational alignment, and systematic weight update changes. To facilitate future research, we release language-specific neuron identifications for over 100 languages as well as our adaptation pipeline, offering a cost-effective pathway for adapting state-of-the-art models to underrepresented languages.

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