| 摘要: |
| 大模型在推理任务中的性能表现显著优于传统模型, 但仍难以应对复杂任务对计算成本、回复质量等方面提出的要求. 在此背景下, 模型互联通过构建模型协作范式实现了大模型能力的共享、整合和互补. 串联架构是一种典型的模型协作形式, 其将多个大模型按照链式顺序进行组合, 以逐级优化的方式增强多模型系统的能力. 模型串联中的路由旨在选择合适的串联路径, 其是提高系统能力的关键因素. 然而, 当前模型串联路由评估与选择缺乏对模型协作关系的系统性考量. 为此, 设计一种基于协作关系的模型动态路由方法. 它首先通过互评量化机制建立模型协作关系图谱, 然后利用动态协作路由算法逐跳分析回复并优化路径选择. 互评量化机制利用梯度互评来分析两两模型协作关系质量. 基于所得协作质量信息, 动态协作路由算法采取模型“一致同意规则”分析每一跳回复并确定路径顺序, 从而支持动态路由调整. 实验结果表明, 在基线任务数据集上, 所提路由算法在准确性和回复胜率等方面优于非预设路由及非针对性路由算法. 在OMGEval数据集上的胜率较非预设路由最大可提升45%. |
| 关键词: 大模型 模型互联 模型协作 串联架构 动态路由 |
| DOI：10.13328/j.cnki.jos.007498 |
| 分类号:TP18 |
| 基金项目:国家自然科学基金国际合作重点项目(W2411053); 国家自然科学基金联合基金重点项目(U23B2027) |
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| Dynamic Model Routing Based on Collaborative Relationship |
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WU Jun-Ru, LI Zhe-Tao, WANG Jian-Hui, LIU Zhong-Ren, PANG Yong-Hao, HUANG Ji-Jun
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College of Information Science and Technology, Jinan University, Guangzhou 510632, China
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| Abstract: |
| Large language models demonstrate significantly superior performance in reasoning tasks compared to traditional models, yet still struggle to meet the demands of complex tasks in terms of computational cost and response quality. Against this backdrop, model interconnection enables the sharing, integration, and complementation of large model capabilities by constructing a collaborative paradigm among models. The cascade architecture represents a typical form of such collaboration, where multiple large models are organized in a chain-like sequence to enhance system performance through step-by-step optimization. Routing in model cascades aims to select appropriate cascade paths and serves as a key factor in improving system capabilities. However, current routing evaluation and selection methods lack systematic consideration of model collaboration relationships. To address this, this study proposes a dynamic routing method based on collaboration relationships. It first builds a model collaboration graph through a mutual evaluation mechanism, and then employs a dynamic collaborative routing algorithm to analyze responses hop by hop and optimize path selection. The mutual evaluation mechanism uses gradient-based mutual assessment to quantify the quality of pairwise model collaboration. Based on the resulting collaboration quality information, the dynamic collaborative routing algorithm adopts a model “consensus rule” to analyze each hop’s response and determine the routing order, thus enabling dynamic path adjustment. Experimental results show that the proposed routing algorithm outperforms both non-preset and non-targeted routing methods in terms of accuracy and response win rate on benchmark task datasets. On the OMGEval dataset, the win rate is improved by up to 45% compared to non-preset routing. |
| Key words: large language model (LLM) model interconnection model collaboration serial architecture dynamic routing |
