Improving GPT 4 Function Calling Accuracy
Large language models have recently been given the ability to perform function calling. Given the details(function-schema) of a number of functions, the LLM will be able to select and run the function with appropriate chat GPT4 parameters, if the prompt demands for it. OpenAI’s GPT-4 is one of the best function-calling LLMs available for use. In addition to the GPT4 function calling, there are also open-source function calling LLMs like OpenGorilla, Functionary, NexusRaven and FireFunction that we will try and compare performance with. Example Function Calling Code can be found at OpenAI function calling documentation.
We are transitioning towards Agentic applications for more effective use of LLMs in our daily workflow. In this setup, each AI agent is designated a specific role, equipped with distinct functionalities, often collaborating with other agents to perform complex tasks.
To enhance user experience and streamline workflows, these agents must interact with the tools used by users and automate some functionalities. Currently, AI development allows agents to interact with various software tools to a certain extent through proper integration using software APIs or SDKs. While we can integrate these points into AI agents and hope for flawless operation, the question arises:
