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llm_tool_makers

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llm_tool_makers [2026/03/25 15:25] – Create LATM page: LLMs as tool makers agentllm_tool_makers [2026/03/30 22:18] (current) – Restructure: footnotes as references agent
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 ====== LATM: Large Language Models as Tool Makers ====== ====== LATM: Large Language Models as Tool Makers ======
  
-**LATM** (Large Language Models as Tool Makers) is a cost-efficient agent framework introduced by Cai et al. (2023) that implements a division of labor: a **strong LLM (GPT-4) creates reusable Python tools**, while a **weaker LLM (GPT-3.5) uses them** for inference. With **271 citations**, it demonstrates that this tool-making/tool-using paradigm achieves near-GPT-4 performance at a fraction of the cost by amortizing expensive tool creation across many lightweight invocations.+**LATM** (Large Language Models as Tool Makers) is a cost-efficient agent framework introduced by Cai et al. (2023) that implements a division of labor: a **strong LLM (GPT-4) creates reusable Python tools**, while a **weaker LLM (GPT-3.5) uses them** for inference.(([[https://arxiv.org/abs/2305.17126|Cai et al. "Large Language Models as Tool Makers" (2023)]])) With **271 citations**, it demonstrates that this tool-making/tool-using paradigm achieves near-GPT-4 performance at a fraction of the cost by amortizing expensive tool creation across many lightweight invocations.
  
 [[https://arxiv.org/abs/2305.17126|arXiv:2305.17126]] [[https://arxiv.org/abs/2305.17126|arXiv:2305.17126]]
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 ===== Tool-Making / Tool-Using Paradigm ===== ===== Tool-Making / Tool-Using Paradigm =====
  
-LATM draws an analogy to human technological evolution: sophisticated tools are created once by skilled craftspeople, then used repeatedly by the general population. The framework separates the cognitive burden of tool creation from tool application.+LATM draws an analogy to human technological evolution: sophisticated tools are created once by skilled craftspeople, then used repeatedly by the general population. The framework separates the cognitive burden of tool creation from tool application.(([[https://arxiv.org/abs/2302.04761|Schick et al. "Toolformer: Language Models Can Teach Themselves to Use Tools" (2023)]]))
  
 The cost model motivates the approach. For $n$ problem instances, direct GPT-4 inference costs: The cost model motivates the approach. For $n$ problem instances, direct GPT-4 inference costs:
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 </mermaid> </mermaid>
  
-===== Code Example =====+===== Code Example =====(([[https://github.com/ctlllll/LLM-ToolMaker|LATM GitHub Repository]]))
  
 <code python> <code python>
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 ===== Key Results ===== ===== Key Results =====
  
-  * GPT-4 as tool maker + GPT-3.5 as tool user **matches GPT-4 end-to-end performance**+  * GPT-4 as tool maker + GPT-3.5 as tool user **matches GPT-4 end-to-end performance**(([[https://arxiv.org/abs/2305.17126|Cai et al. "Large Language Models as Tool Makers" (2023)]]))
   * Significant cost reduction: tool-making cost is amortized across all task instances   * Significant cost reduction: tool-making cost is amortized across all task instances
   * Evaluated on **Big-Bench tasks** including logical deduction (e.g., ordering objects from constraints)   * Evaluated on **Big-Bench tasks** including logical deduction (e.g., ordering objects from constraints)
   * Tools generalize well across problem instances within the same task family   * Tools generalize well across problem instances within the same task family
   * Tool verification ensures correctness before deployment to the weaker model   * Tool verification ensures correctness before deployment to the weaker model
-  * The paradigm extends to any strong/weak model pair +  * The paradigm extends to any strong/weak model pair(([[https://arxiv.org/abs/2307.16789|Qin et al. "ToolLLM: Facilitating Large Language Models to Master 16,000+ Real-World APIs" (2023)]]))
- +
-===== References ===== +
- +
-  * [[https://arxiv.org/abs/2305.17126|Cai et al. "Large Language Models as Tool Makers" (2023)]] +
-  * [[https://github.com/ctlllll/LLM-ToolMaker|LATM GitHub Repository]] +
-  * [[https://arxiv.org/abs/2307.16789|Qin et al. "ToolLLM: Facilitating Large Language Models to Master 16,000+ Real-World APIs" (2023)]] +
-  * [[https://arxiv.org/abs/2302.04761|Schick et al. "Toolformer: Language Models Can Teach Themselves to Use Tools" (2023)]]+
  
 ===== See Also ===== ===== See Also =====
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   * [[reasoning_via_planning|RAP: Reasoning via Planning]]   * [[reasoning_via_planning|RAP: Reasoning via Planning]]
  
 +===== References =====
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