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This paper proposes the Tool-Planner framework to address the excessive error correction and multi-tool planning challenges associated with tool learning for large-scale language models (LLMs). Tool-Planner groups tools based on API functions with similar functionality into toolkits, allowing LLMs to plan across multiple toolkits. When tool errors occur, the language model can reselect and adjust tools based on the toolkit. Experimental results show that the tool-planning framework optimizes tool learning, demonstrating high success rates on diverse datasets, including models like GPT-4 and Claude 3. The source code is publicly available.
Takeaways, Limitations
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Takeaways:
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We present a Tool-Planner framework that effectively addresses the problems of excessive error correction and inefficient planning that arise during the LLM tool learning process.
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Improve tool selection and planning strategies for LLMs through toolkit-based tool grouping to increase task success.
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Performance improvements were verified through experiments on various LLM models, including GPT-4 and Claude 3.
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Ensure reproducibility and extensibility through open source code.
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Limitations:
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Further research is needed to determine the generality of the proposed toolkit architecture and its applicability to different types of tasks.
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The toolkit's dependence on specific API functions may limit the tool's diversity.
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There may be a lack of generalized performance evaluation based on experimental results for specific LLM models.
