让大模型更聪明地决定何时用工具，减少浪费
Alignment for Efficient Tool Calling of Large Language Models
- 通过概率估计知识边界，判断是否该调用工具
- 实验显示工具调用效率提升，无效调用显著减少
- 适合需要高效使用外部工具的AI应用开发者
近期工具学习进展使大语言模型（LLMs）能够集成外部工具，通过拓展知识边界提升任务表现。然而，依赖工具常带来性能、速度与成本之间的权衡，模型可能过度依赖并高估工具效果。本文提出一种多目标对齐框架，结合概率性知识边界估计与动态决策机制，帮助模型根据置信度更智能地判断是否调用工具。框架包含基于一致性的知识边界估计和绝对估计两种方法，以及两种将估计结果融入模型决策的训练策略。在多种工具调用场景下的实验表明，该框架有效提升了工具使用效率，显著减少了不必要的工具调用。
原文摘要 · Abstract (English)
Recent advancements in tool learning have enabled large language models (LLMs) to integrate external tools, enhancing their task performance by expanding their knowledge boundaries. However, relying on tools often introduces tradeoffs between performance, speed, and cost, with LLMs sometimes exhibiting overreliance and overconfidence in tool usage. This paper addresses the challenge of aligning LLMs with their knowledge boundaries to make more intelligent decisions about tool invocation. We propose a multi objective alignment framework that combines probabilistic knowledge boundary estimation with dynamic decision making, allowing LLMs to better assess when to invoke tools based on their confidence. Our framework includes two methods for knowledge boundary estimation, consistency based and absolute estimation, and two training strategies for integrating these estimates into the model decision making process. Experimental results on various tool invocation scenarios demonstrate the effectiveness of our framework, showing significant improvements in tool efficiency by reducing unnecessary tool usage.
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