用专家协作架构让普通人也能参与大模型开发
MoECollab: Democratizing LLM Development Through Collaborative Mixture of Experts
- 把大模型拆成多个专业模块，由智能路由系统协调
- 准确率提升3-7%，计算量减少34%，专家分工后效果更好
- 适合资源有限的研究者或团队共同开发模型
大语言模型开发日益集中化，限制了资源不足者的参与。本文提出MoECollab框架，基于混合专家（MoE）架构实现分布式协同建模。通过将单一模型分解为由可训练门控网络协调的专业化专家模块，该框架使不同计算能力的贡献者均可参与。我们提供了完整的技术实现与专家动态、门控机制和集成策略的数学基础。在多个数据集上的实验表明，相比基线模型，本方法准确率提升3%-7%，计算需求降低34%。专家专业化带来显著领域优势：通用分类任务中F1分数从51%提升至88%，新闻分类准确率从23%升至44%。我们形式化了路由熵优化问题，证明合理正则化可使专家利用率提高14%。结果验证了MoECollab通过架构支持协作，在推动大模型开发民主化方面的有效性。
原文摘要 · Abstract (English)
Large Language Model (LLM) development has become increasingly centralized, limiting participation to well-resourced organizations. This paper introduces MoECollab, a novel framework leveraging Mixture of Experts (MoE) architecture to enable distributed, collaborative LLM development. By decomposing monolithic models into specialized expert modules coordinated by a trainable gating network, our framework allows diverse contributors to participate regardless of computational resources. We provide a complete technical implementation with mathematical foundations for expert dynamics, gating mechanisms, and integration strategies. Experiments on multiple datasets demonstrate that our approach achieves accuracy improvements of 3-7% over baseline models while reducing computational requirements by 34%. Expert specialization yields significant domain-specific gains, with improvements from 51% to 88% F1 score in general classification and from 23% to 44% accuracy in news categorization. We formalize the routing entropy optimization problem and demonstrate how proper regularization techniques lead to 14% higher expert utilization rates. These results validate MoECollab as an effective approach for democratizing LLM development through architecturally-supported collaboration.
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