ToolPlanner: A Tool Augmented LLM for Multi Granularity Instructions with Path Planning and Feedback
Abstract
Recently, tool-augmented LLMs have gained increasing attention. Given an instruction, tool-augmented LLMs can interact with various external tools in multiple rounds and provide a final answer. However, previous LLMs were trained on overly detailed instructions, which included API names or parameters, while real users would not explicitly mention these API details. This leads to a gap between trained LLMs and real-world scenarios. In addition, most works ignore whether the interaction process follows the instruction. To address these issues, we constructed a training dataset called MGToolBench, which contains statement and category-level instructions to better reflect real-world scenarios. In addition, we propose ToolPlanner, a two-stage reinforcement learning framework that utilizes path planning and two feedback mechanisms to enhance the LLM’s task completion and instruction-following capabilities. Experimental results show that ToolPlanner significantly improves the Match Rate, Pass Rate and Win Rate by 26.8%, 20.2%, and 5.6% compared to the SOTA model. Human evaluation verifies that the multi-granularity instructions can better align with users’ usage habits. Our data and code will be released upon acceptance.- Anthology ID:
- 2024.emnlp-main.1018
- Volume:
- Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2024
- Address:
- Miami, Florida, USA
- Editors:
- Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 18315–18339
- Language:
- URL:
- https://preview.aclanthology.org/add_missing_videos/2024.emnlp-main.1018/
- DOI:
- 10.18653/v1/2024.emnlp-main.1018
- Cite (ACL):
- Qinzhuo Wu, Wei Liu, Jian Luan, and Bin Wang. 2024. ToolPlanner: A Tool Augmented LLM for Multi Granularity Instructions with Path Planning and Feedback. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 18315–18339, Miami, Florida, USA. Association for Computational Linguistics.
- Cite (Informal):
- ToolPlanner: A Tool Augmented LLM for Multi Granularity Instructions with Path Planning and Feedback (Wu et al., EMNLP 2024)
- PDF:
- https://preview.aclanthology.org/add_missing_videos/2024.emnlp-main.1018.pdf