Consensus-Aligned Neuron Efficient Fine-Tuning Large Language Models for Multi-Domain Machine Translation

• URL: http://arxiv.org/abs/2602.05694v1
• Date: Thu, 05 Feb 2026 14:20:59 GMT
• Title: Consensus-Aligned Neuron Efficient Fine-Tuning Large Language Models for Multi-Domain Machine Translation
• Authors: Shuting Jiang, Ran Song, Yuxin Huang, Yan Xiang, Yantuan Xian, Shengxiang Gao, Zhengtao Yu,
• Abstract summary: Multi-domain machine translation (MDMT) aims to build a unified model capable of translating content across diverse domains.<n> domain adaptation remains a challenge for large language models (LLMs)<n>We propose a neuron-efficient fine-tuning framework for MDMT that identifies and updates consensus-aligned neurons within LLMs.
• Score: 26.913797159189787
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-domain machine translation (MDMT) aims to build a unified model capable of translating content across diverse domains. Despite the impressive machine translation capabilities demonstrated by large language models (LLMs), domain adaptation still remains a challenge for LLMs. Existing MDMT methods such as in-context learning and parameter-efficient fine-tuning often suffer from domain shift, parameter interference and limited generalization. In this work, we propose a neuron-efficient fine-tuning framework for MDMT that identifies and updates consensus-aligned neurons within LLMs. These neurons are selected by maximizing the mutual information between neuron behavior and domain features, enabling LLMs to capture both generalizable translation patterns and domain-specific nuances. Our method then fine-tunes LLMs guided by these neurons, effectively mitigating parameter interference and domain-specific overfitting. Comprehensive experiments on three LLMs across ten German-English and Chinese-English translation domains evidence that our method consistently outperforms strong PEFT baselines on both seen and unseen domains, achieving state-of-the-art performance.

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