The Power of Large Language Models in Tool Creation

💡 The availability of external tools greatly enhances the problem-solving abilities of large language models.

❓ How can we create specialized tools for specific problems that large language models haven't encountered before?

🔧 The authors of the paper propose a solution to this problem by having the large language models create their own tools for problem-solving.

🔧 The closed loop framework uses two different large language models: the tool making LLM and the tool using LLM.

💼 The tool making LLM needs to be the more capable or expensive model, while the tool using LLM can be lighter weight or cheaper.

💡 Once a reusable tool is created, the lighter weight or cheaper tool using LLM can be used to solve future tasks of the same category.

💡 The tool building process involves three steps: proposing a tool, generating code using a language model, and verifying its functionality with unit tests.

🔍 To propose a tool, short examples and training samples are used to construct a prompt for the language model to generate code.

✅ After generating the code, the language model is used to write unit tests and ensure the functionality of the tool.

📝 The final step is to wrap up the tool, allowing it to be used for future tasks by mapping their solutions to the tool instead of relying on the language model.

🌐 A concrete example is given, illustrating how the tool building process can be applied to solve a specific ordering problem.

🔑 Large language models can be used as tools to solve specific problems.

💡 To create a tool, a prompt can be constructed to generate code that solves a class of problems.

🔄 Once the code is generated, a lighter weight language model can be used to solve similar problems.

🔑 Large language models can be used as tools to solve complex problems.

⚙️ This can be achieved by re-expressing the problem conditions as inputs to a Python method.

🧩 A logic called a dispatcher is utilized to determine if a tool exists for a specific problem.

🔑 Large language models (LLMs) can automate the process of building tools for problem-solving.

🧠 LLMs, like gpt4, can generate new tools that smaller and cheaper LLMs can use to solve problems.

⚖️ Using the heavier weight LLM to build the tools and the lighter weight LLM to solve problems reduces the overall cost.

📚 Language models are becoming larger and more expensive.

💡 The performance of these large language models is being discussed.