Summary of Toolplanner: a Tool Augmented Llm For Multi Granularity Instructions with Path Planning and Feedback, by Qinzhuo Wu et al.
ToolPlanner: A Tool Augmented LLM for Multi Granularity Instructions with Path Planning and Feedback
by Qinzhuo Wu, Wei Liu, Jian Luan, Bin Wang
First submitted to arxiv on: 23 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach is proposed to enhance the performance of tool-augmented large language models (LLMs) in real-world scenarios. Existing LLMs were trained on detailed instructions that included API names or parameters, which may not accurately reflect how users interact with these tools. To address this gap, a training dataset called MGToolBench was constructed, featuring statement and category-level instructions that better mimic real-world usage. Additionally, the ToolPlanner framework is introduced, utilizing path planning and two feedback mechanisms to improve task completion and instruction-following capabilities. Experimental results demonstrate significant improvements in metrics such as Match Rate, Pass Rate, and Win Rate compared to state-of-the-art models. Human evaluation confirms that multi-granularity instructions better align with users’ habits. The proposed approach has the potential to enhance the effectiveness of tool-augmented LLMs in various applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Tool-augmented large language models (LLMs) are getting smarter! These AI systems can work with external tools to get answers. But, they were trained on really detailed instructions that might not be what people use in real life. To make them better, researchers created a new training dataset and a special framework called ToolPlanner. This helps the LLMs follow instructions and complete tasks more effectively. The results show that this approach is much better than other methods, and people think it’s a good way to improve how LLMs work. |
