让大模型学会跨工具通用使用，解决新工具不适应问题。
MetaToolAgent: Towards Generalizable Tool Usage in LLMs through Meta-Learning
- 用元学习方法训练模型，提升对未见工具的泛化能力。
- 在155个工具、9377组问答上测试，新工具表现显著优于基线。
- 适合需要动态调用多种工具的智能系统研发人员。
工具学习对大语言模型（LLMs）有效协调和利用多样化工具以解决复杂现实任务日益重要。通过选择并集成合适的工具，LLMs可突破纯语言理解范畴，实现专业化功能。然而，现有工具选择方法多局限于有限工具集，在实际部署中遇到新工具时难以泛化。为此，我们构建了一个覆盖7个领域、包含155个工具和9,377组问答对的综合性数据集，模拟真实集成场景。同时提出MetaToolAgent（MTA），一种旨在提升跨工具泛化的元学习方法。实验结果表明，MTA在未见过的工具上显著优于基线方法，展现出构建灵活可扩展动态工具协调系统的重要潜力。
原文摘要 · Abstract (English)
Tool learning is increasingly important for large language models (LLMs) to effectively coordinate and utilize a diverse set of tools in order to solve complex real-world tasks. By selecting and integrating appropriate tools, LLMs extend their capabilities beyond pure language understanding to perform specialized functions. However, existing methods for tool selection often focus on limited tool sets and struggle to generalize to novel tools encountered in practical deployments. To address these challenges, we introduce a comprehensive dataset spanning 7 domains, containing 155 tools and 9,377 question-answer pairs, which simulates realistic integration scenarios. Additionally, we propose MetaToolAgent (MTA), a meta-learning approach designed to improve cross-tool generalization. Experimental results show that MTA significantly outperforms baseline methods on unseen tools, demonstrating its promise for building flexible and scalable systems that require dynamic tool coordination.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。