實現靈活的多大型語言模型整合以支持可擴展的知識聚合
Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation
May 28, 2025
作者: Zhenglun Kong, Zheng Zhan, Shiyue Hou, Yifan Gong, Xin Meng, Pengwei Sui, Peiyan Dong, Xuan Shen, Zifeng Wang, Pu Zhao, Hao Tang, Stratis Ioannidis, Yanzhi Wang
cs.AI
摘要
大型语言模型（LLMs）展现了显著的潜力，但通过传统的微调方法持续提升其性能仍面临挑战，尤其是在整合其他专业LLMs的能力时。流行的集成和权重融合方法需要大量内存，且难以适应不断变化的数据环境。近期研究尝试将多个LLMs的知识转移至单一目标模型，然而，这些方法因候选模型选择及训练流程的灵活性不足，常导致任务间的干扰与性能下降。为解决这些问题，我们提出了一种框架，该框架能自适应地选择并聚合来自不同LLMs的知识，构建一个更强且单一的模型，从而避免集成方法的高内存开销及权重融合的僵化性。具体而言，我们设计了一个自适应选择网络，该网络根据评分识别最相关的源LLMs，从而减少知识干扰。此外，我们提出了一种动态加权融合策略，该策略考虑了候选LLMs的固有优势，并引入了一种反馈驱动的损失函数，以防止选择器收敛于单一源子集。实验结果表明，与现有方法相比，我们的方法能够实现更稳定、可扩展的知识聚合过程，同时将知识干扰减少高达50%。代码可在https://github.com/ZLKong/LLM_Integration获取。
English
Large language models (LLMs) have shown remarkable promise but remain
challenging to continually improve through traditional finetuning, particularly
when integrating capabilities from other specialized LLMs. Popular methods like
ensemble and weight merging require substantial memory and struggle to adapt to
changing data environments. Recent efforts have transferred knowledge from
multiple LLMs into a single target model; however, they suffer from
interference and degraded performance among tasks, largely due to limited
flexibility in candidate selection and training pipelines. To address these
issues, we propose a framework that adaptively selects and aggregates knowledge
from diverse LLMs to build a single, stronger model, avoiding the high memory
overhead of ensemble and inflexible weight merging. Specifically, we design an
adaptive selection network that identifies the most relevant source LLMs based
on their scores, thereby reducing knowledge interference. We further propose a
dynamic weighted fusion strategy that accounts for the inherent strengths of
candidate LLMs, along with a feedback-driven loss function that prevents the
selector from converging on a single subset of sources. Experimental results
demonstrate that our method can enable a more stable and scalable knowledge
aggregation process while reducing knowledge interference by up to 50% compared
to existing approaches. Code is avaliable at
https://github.com/ZLKong/LLM_Integration