SWRouter：面向多轮大语言模型对话的相似性收缩窗口路由

中文摘要 AI 辅助

大语言模型展现出互补的优势，这促使了路由方法的发展，即将每个查询分派给最合适的模型。尽管现有的路由器在单轮设置中有效，但它们无法直接迁移到多轮对话中，因为在多轮对话中，路由性能关键取决于历史上下文如何被分段、保留并整合到当前提示中。这带来了两个基本挑战：在上下文构建过程中防止信息丢失和信息混淆，以及在不将模型选择与提示构建质量混为一谈的情况下评估路由质量。在本文中，我们提出了SWRouter，一种用于多轮大语言模型路由的相似性收缩窗口路由器。SWRouter将基于相似性的上下文分段机制用于提示构建，并结合了一个双指标评估框架，该框架将构建准确性与路由器性能解耦。在多轮对话基准上的实验表明，SWRouter持续超越强基线，在评估准确率上比最佳单个大语言模型提高了16.26%，比Conv-ID上下文基线额外提高了8.22%。我们的结果强调，多轮大语言模型路由需要对上下文构建和评估进行联合设计，而不是对单轮路由方法的直接扩展。

英文摘要

Large language models exhibit complementary strengths, motivating routing methods that dispatch each query to the most suitable model. Although existing routers are effective in single-turn settings, they do not directly transfer to multi-turn dialogue, where routing performance critically depends on how historical context is segmented, retained, and incorporated into the current prompt. This introduces two fundamental challenges: preventing information loss and information confusion during context construction, and evaluating routing quality without conflating model selection with prompt construction quality. In this paper, we propose SWRouter, a Similarity-Contractive Window Router for multi-turn large language model routing. SWRouter combines a similarity-based context segmentation mechanism for prompt construction with a dual-metric evaluation framework that decouples construction accuracy from router performance. Experiments on multi-turn dialogue benchmarks demonstrate that SWRouter consistently surpasses strong baselines, achieving a 16.26% improvement in evaluation accuracy over the best individual large language model and an additional 8.22% gain over the Conv-ID Context baseline. Our results highlight that multi-turn large language model routing requires a joint design of context construction and evaluation, rather than a direct extension of single-turn routing methods.

发表机构

• Shanghai Jiao Tong University（上海交通大学）
• Baidu Inc.（百度公司）
• Wilfrid Laurier University（劳里埃大学）
• The Hang Seng University of Hong Kong（香港恒生大学）

机构由 AI 辅助整理，请以论文原文为准。