ToolGen: Unified Tool Retrieval and Calling via Generation
Wang, Renxi Han, Xudong Ji, Lei Wang, Shu Baldwin, Timothy Li, Haonan
Wang, Renxi
Han, Xudong
Ji, Lei
Wang, Shu
Baldwin, Timothy
Li, Haonan
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Natural Language Processing
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Conference proceeding
Date
2025
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English
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Abstract
As large language models (LLMs) advance, their inability to autonomously execute tasks by directly interacting with external tools remains a critical limitation. Traditional methods rely on inputting tool descriptions as context, which is constrained by context length and requires separate, often inefficient, retrieval mechanisms. We introduce ToolGen, a paradigm shift that integrates tool knowledge directly into the LLM's parameters by representing each tool as a unique token. This enables the LLM to generate tool calls and arguments as part of its next token prediction capabilities, seamlessly blending tool invocation with language generation. Our framework allows the LLM to access and utilize a vast amount of tools with no additional retrieval step, significantly enhancing both performance and scalability. Experimental results with over 47,000 tools show that ToolGen not only achieves superior results in both tool retrieval and autonomous task completion but also sets the stage for a new era of AI agents that can adapt to tools across diverse domains. By fundamentally transforming tool retrieval into a generative process, ToolGen paves the way for more versatile, efficient, and autonomous AI systems. ToolGen enables end-to-end tool learning and opens opportunities for integration with other advanced techniques such as chain-of-thought and reinforcement learning, thereby expanding the practical capabilities of LLMs. © 2025 13th International Conference on Learning Representations, ICLR 2025. All rights reserved.
Citation
R. Wang, X. Han, L. Ji, S. Wang, T. Baldwin, and H. Li, “ToolGen: Unified Tool Retrieval and Calling via Generation,” International Conference on Representation Learning, vol. 2025, pp. 73473–73498, May 2025
Source
13th International Conference on Learning Representations, ICLR 2025
Conference
13th International Conference on Learning Representations, ICLR 2025
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Source
13th International Conference on Learning Representations, ICLR 2025
Publisher
International Conference on Learning Representations, ICLR