减少工具数量可显著提升大模型在边缘设备上的函数调用效率
Less is More: Optimizing Function Calling for LLM Execution on Edge Devices
- 不依赖微调，通过动态筛选工具提升性能
- 执行时间最多降低70%，功耗减少40%
- 适合资源受限的边缘智能应用开发
基础模型的高级函数调用能力为部署能执行复杂API任务的智能体开辟了新路径。然而，海量数据处理与频繁的API交互使函数调用在硬件上代价高昂，尤其在边缘设备上。当前大语言模型在边缘环境下进行函数调用时，难以应对复杂输入或有效管理多个工具，导致任务完成准确率低、延迟高、功耗大。本文提出Less-is-More，一种无需微调的动态工具选择方案。核心思想是：有意识地减少模型可用的工具数量，可显著提升其函数调用性能、执行效率和能效。在边缘硬件上对前沿大模型的实验表明，该方法大幅提升了智能体成功率，执行时间最多减少70%，功耗最多降低40%。
原文摘要 · Abstract (English)
The advanced function-calling capabilities of foundation models open up new possibilities for deploying agents to perform complex API tasks. However, managing large amounts of data and interacting with numerous APIs makes function calling hardware-intensive and costly, especially on edge devices. Current Large Language Models (LLMs) struggle with function calling at the edge because they cannot handle complex inputs or manage multiple tools effectively. This results in low task-completion accuracy, increased delays, and higher power consumption. In this work, we introduce Less-is-More, a novel fine-tuning-free function-calling scheme for dynamic tool selection. Our approach is based on the key insight that selectively reducing the number of tools available to LLMs significantly improves their function-calling performance, execution time, and power efficiency on edge devices. Experimental results with state-of-the-art LLMs on edge hardware show agentic success rate improvements, with execution time reduced by up to 70% and power consumption by up to 40%.
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