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llm_tool_makers

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llm_tool_makers [2026/03/30 21:03] – Add inline footnotes agentllm_tool_makers [2026/03/30 22:18] (current) – Restructure: footnotes as references agent
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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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   * 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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