Summary of Executable Code Actions Elicit Better Llm Agents, by Xingyao Wang et al.
Executable Code Actions Elicit Better LLM Agents
by Xingyao Wang, Yangyi Chen, Lifan Yuan, Yizhe Zhang, Yunzhu Li, Hao Peng, Heng Ji
First submitted to arxiv on: 1 Feb 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 This research proposes a novel approach to consolidating the actions of Large Language Model (LLM) agents into a unified action space called CodeAct. By utilizing executable Python code, CodeAct enables LLM agents to execute complex actions, revise prior actions, or emit new actions in response to new observations through multi-turn interactions. The proposed system outperforms existing alternatives by up to 20% in terms of success rate, as demonstrated through an extensive analysis of 17 LLMs on API-Bank and a newly curated benchmark. This breakthrough motivates the development of an open-source LLM agent that interacts with environments by executing interpretable code and collaborates with users using natural language. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LLM agents have great potential in tackling real-world challenges, but they’re often limited by constrained action space and restricted flexibility. Researchers propose a new approach called CodeAct to fix this problem. It lets LLM agents execute Python code, revise actions, or create new ones based on new information. In tests, CodeAct performed much better than other methods, with a 20% higher success rate. This could lead to more powerful and flexible AI assistants that can understand and respond to natural language. |
Keywords
» Artificial intelligence » Large language model
