The Power of Large Language Models in Tool Creation
This video explores how large language models can create tailored tools for problem-solving independently.
00:00:00 This video discusses how external tools can enhance the problem-solving abilities of large language models. The authors propose a method for the models to create tailored tools for problem-solving independently.
💡 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.
00:01:05 This video discusses the concept of using large language models to create reusable tools for solving similar tasks, with the tool maker model being more capable and expensive than the tool user model.
🔧 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.
00:02:09 The video discusses the process of creating tools using large language models, including proposing tools, writing code and unit tests, and wrapping them up for future use.
💡 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.
00:03:14 Creating a tool using language models to generate a Python function that finds the order of objects based on given conditions.
🔑 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.
00:04:18 How language models can be used as tools by expressing conditions as inputs to Python methods and using a dispatcher to determine if a tool exists. GPT 3.5 can match or surpass GPT4's performance.
🔑 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.
00:05:25 This video discusses the use of large language models (LLMs) as tool makers and how they can automate the process of building new tools. The paper showcases the success of using LLMs to generate tools for solving various problems.
🔑 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.
00:06:30 This video explores the capabilities of large language models as powerful tools for various applications.
📚 Language models are becoming larger and more expensive.
💡 The performance of these large language models is being discussed.
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