Summary of Catp-llm: Empowering Large Language Models For Cost-aware Tool Planning, by Duo Wu et al.
CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning
by Duo Wu, Jinghe Wang, Yuan Meng, Yanning Zhang, Le Sun, Zhi Wang
First submitted to arxiv on: 25 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 The proposed Cost-Aware Tool Planning with LLMs (CATP-LLM) framework enables large language models to consider tool execution costs for developing general AI systems. By incorporating a tool planning language, CATP-LLM generates non-sequential plans for efficient concurrent tool execution and cost reduction. A cost-aware offline reinforcement learning algorithm optimizes the performance-cost trade-off in tool planning. The authors present OpenCATP, a platform for evaluating cost-aware planning, and demonstrate that CATP-LLM outperforms GPT-4 with an average improvement of 28.2%-30.2% higher plan performance and 24.7%-45.8% lower costs. This framework has the potential to revolutionize tool planning in AI systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way for artificial intelligence (AI) to use tools more efficiently. Right now, AI models don’t consider how much time or effort it takes to use each tool when trying to complete a task. To fix this, the authors created a system called CATP-LLM that thinks about both the cost and performance of using different tools. This helps AI make better decisions about which tools to use and when. The authors tested their idea and found that it worked really well, even beating other popular AI models like GPT-4. |
Keywords
» Artificial intelligence » Gpt » Reinforcement learning
