通过识别共识神经元，实现大模型多领域翻译的高效微调。
Consensus-Aligned Neuron Efficient Fine-Tuning Large Language Models for Multi-Domain Machine Translation
- 基于神经元行为与领域特征的互信息最大化，筛选出跨域共识神经元。
- 在10个德英、中英翻译领域上，对已见和未见领域均超越现有方法。
- 适合需要高效适配多领域翻译的大模型应用开发者使用。
多领域机器翻译（MDMT）旨在构建一个能处理多样化领域内容的统一模型。尽管大型语言模型（LLMs）展现出强大的翻译能力，但领域适应仍是其挑战。现有方法如上下文学习和参数高效微调常面临领域偏移、参数干扰和泛化能力有限的问题。本文提出一种神经元高效的微调框架，通过最大化神经元行为与领域特征间的互信息，识别并更新一致性对齐的神经元，使模型同时捕捉通用翻译模式与领域特异性细节。该方法引导模型微调，有效缓解参数干扰和领域过拟合。在三个大型语言模型上，针对十组德英和中英翻译领域进行的全面实验表明，本方法在已见与未见领域上均持续优于强基线，达到当前最佳性能。
原文摘要 · Abstract (English)
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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