[R] LLMs in the Imaginarium: Tool Learning through Simulated Trial and Error - Microsoft Semantic Machines 2024 - Giving Mistral-Instruct-7B a boost of 46,7% points and enabling it to outperform GPT-4 in the ToolBench benchmark!

Paper: [https://arxiv.org/abs/2403.04746](https://arxiv.org/abs/2403.04746)

Github: [https://github.com/microsoft/simulated-trial-and-error](https://github.com/microsoft/simulated-trial-and-error)

Abstract:

>Tools are essential for large language models (LLMs) to acquire up-to-date information and take consequential actions in external environments. Existing work on tool-augmented LLMs primarily focuses on the broad coverage of tools and the flexibility of adding new tools. However, a critical aspect that has surprisingly been understudied is simply how accurately an LLM uses tools for which it has been trained. We find that existing LLMs, including GPT-4 and open-source LLMs specifically fine-tuned for …

abstract augmented llms coverage environments flexibility however information language language models large language large language models llm llms machinelearning tool tools work