Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.13381 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911926330589184 |
|---|---|
| author | Hou, Xinming Yang, Mingming Jiao, Wenxiang Wang, Xing Tu, Zhaopeng Zhao, Wayne Xin |
| author_facet | Hou, Xinming Yang, Mingming Jiao, Wenxiang Wang, Xing Tu, Zhaopeng Zhao, Wayne Xin |
| contents | Existing LLMs exhibit remarkable performance on various NLP tasks, but still struggle with complex real-world tasks, even equipped with advanced strategies like CoT and ReAct. In this work, we propose the CoAct framework, which transfers the hierarchical planning and collaboration patterns in human society to LLM systems. Specifically, our CoAct framework involves two agents: (1) A global planning agent, to comprehend the problem scope, formulate macro-level plans and provide detailed sub-task descriptions to local execution agents, which serves as the initial rendition of a global plan. (2) A local execution agent, to operate within the multi-tier task execution structure, focusing on detailed execution and implementation of specific tasks within the global plan. Experimental results on the WebArena benchmark show that CoAct can re-arrange the process trajectory when facing failures, and achieves superior performance over baseline methods on long-horizon web tasks. Code is available at https://github.com/xmhou2002/CoAct. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_13381 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CoAct: A Global-Local Hierarchy for Autonomous Agent Collaboration Hou, Xinming Yang, Mingming Jiao, Wenxiang Wang, Xing Tu, Zhaopeng Zhao, Wayne Xin Computation and Language Existing LLMs exhibit remarkable performance on various NLP tasks, but still struggle with complex real-world tasks, even equipped with advanced strategies like CoT and ReAct. In this work, we propose the CoAct framework, which transfers the hierarchical planning and collaboration patterns in human society to LLM systems. Specifically, our CoAct framework involves two agents: (1) A global planning agent, to comprehend the problem scope, formulate macro-level plans and provide detailed sub-task descriptions to local execution agents, which serves as the initial rendition of a global plan. (2) A local execution agent, to operate within the multi-tier task execution structure, focusing on detailed execution and implementation of specific tasks within the global plan. Experimental results on the WebArena benchmark show that CoAct can re-arrange the process trajectory when facing failures, and achieves superior performance over baseline methods on long-horizon web tasks. Code is available at https://github.com/xmhou2002/CoAct. |
| title | CoAct: A Global-Local Hierarchy for Autonomous Agent Collaboration |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2406.13381 |