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【第216期】LLMSelector：选择不同模型做不同任务
Seventy3：借助NotebookLM的能力进行论文解读，专注人工智能、大模型、机器人算法方向，让大家跟着AI一起进步。
进群添加小助手微信：seventy3_podcast
备注：小宇宙
今天的主题是：Optimizing Model Selection for Compound AI SystemsSummary
This paper addresses the challenge of selecting the best large language models (LLMs) for each component within compound AI systems. Recognizing that different LLMs excel at different sub-tasks, the authors introduce LLMSelector, a framework that efficiently identifies high-performing model allocations. LLMSelector iteratively evaluates and assigns LLMs to individual modules based on estimated module-wise performance. Experiments on various compound systems, utilizing models like GPT-4o and Claude 3.5, demonstrate that LLMSelector achieves significant accuracy gains compared to using a single LLM throughout. Ultimately, the research highlights the importance of strategic model selection for optimizing the overall effectiveness of complex AI systems.
本文聚焦于复合型AI系统中如何为各个组件选择最合适的大型语言模型（LLMs）这一关键挑战。鉴于不同LLMs在子任务上的表现各有优劣，作者提出了 LLMSelector 框架，用于高效识别并分配性能优异的模型到各个模块。LLMSelector通过迭代评估模块级性能，智能地将不同模型分配给最合适的任务模块。
在多个复合系统的实验中，研究使用了如GPT-4o和Claude 3.5等模型，结果表明，LLMSelector相较于统一使用单一模型的方法，在准确性上取得了显著提升。该研究强调了战略性模型选择对于优化复杂AI系统整体效果的重要性，为提升多模型系统的协同效能提供了有力思路。
原文链接：https://arxiv.org/abs/2502.14815
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By 任雨山Seventy3：借助NotebookLM的能力进行论文解读，专注人工智能、大模型、机器人算法方向，让大家跟着AI一起进步。
进群添加小助手微信：seventy3_podcast
备注：小宇宙
今天的主题是：Optimizing Model Selection for Compound AI SystemsSummary
This paper addresses the challenge of selecting the best large language models (LLMs) for each component within compound AI systems. Recognizing that different LLMs excel at different sub-tasks, the authors introduce LLMSelector, a framework that efficiently identifies high-performing model allocations. LLMSelector iteratively evaluates and assigns LLMs to individual modules based on estimated module-wise performance. Experiments on various compound systems, utilizing models like GPT-4o and Claude 3.5, demonstrate that LLMSelector achieves significant accuracy gains compared to using a single LLM throughout. Ultimately, the research highlights the importance of strategic model selection for optimizing the overall effectiveness of complex AI systems.
本文聚焦于复合型AI系统中如何为各个组件选择最合适的大型语言模型（LLMs）这一关键挑战。鉴于不同LLMs在子任务上的表现各有优劣，作者提出了 LLMSelector 框架，用于高效识别并分配性能优异的模型到各个模块。LLMSelector通过迭代评估模块级性能，智能地将不同模型分配给最合适的任务模块。
在多个复合系统的实验中，研究使用了如GPT-4o和Claude 3.5等模型，结果表明，LLMSelector相较于统一使用单一模型的方法，在准确性上取得了显著提升。该研究强调了战略性模型选择对于优化复杂AI系统整体效果的重要性，为提升多模型系统的协同效能提供了有力思路。
原文链接：https://arxiv.org/abs/2502.14815