Shi Shiwei
2024
TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Industry Systems
Yilun Kong
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Jingqing Ruan
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YiHong Chen
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Bin Zhang
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Tianpeng Bao
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Shi Shiwei
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du Guo Qing
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Xiaoru Hu
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Hangyu Mao
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Ziyue Li
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Xingyu Zeng
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Rui Zhao
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Xueqian Wang
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track
Large Language Models (LLMs) have demonstrated proficiency in addressing tasks that necessitate a combination of task planning and the usage of external tools, such as weather and calculator APIs. However, real-world industrial systems present prevalent challenges in task planning and tool usage: numerous APIs in the real system make it intricate to invoke the appropriate one, while the inherent limitations of LLMs pose challenges in orchestrating an accurate sub-task sequence and API-calling order. This paper introduces a comprehensive framework aimed at enhancing the Task Planning and Tool Usage (TPTU) abilities of LLM-based agents in industry. Our framework comprises three key components designed to address these challenges: (1) the API Retriever selects the most pertinent APIs among the extensive API set; (2) the Demo Selector retrieves task-level demonstrations, which is further used for in-context learning to aid LLMs in accurately decomposing subtasks and effectively invoking hard-to-distinguish APIs; (3) LLM Finetuner tunes a base LLM to enhance its capability for task planning and API calling. We validate our methods using a real-world industry system and an open-sourced academic dataset, demonstrating the efficacy of each individual component as well as the integrated framework. The code is available at here.
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Co-authors
- Yilun Kong 1
- Jingqing Ruan 1
- Yihong Chen 1
- Bin Zhang 1
- Tianpeng Bao 1
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