Summary of Planning with Multi-constraints Via Collaborative Language Agents, by Cong Zhang et al.
Planning with Multi-Constraints via Collaborative Language Agents
by Cong Zhang, Derrick Goh Xin Deik, Dexun Li, Hao Zhang, Yong Liu
First submitted to arxiv on: 26 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); 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 paper introduces Planning with Multi-Constraints (PMC), a zero-shot methodology for collaborative large language model-based agents (LLM) to simplify complex task planning with constraints by decomposing it into a hierarchy of subordinate tasks. This approach maps each subtask into executable actions, enabling the efficient planning of real-world tasks. The proposed method is assessed on two constraint-intensive benchmarks, TravelPlanner and API-Bank, achieving an average 42.68% success rate on TravelPlanner and outperforming GPT-4 with ReAct on API-Bank by 13.64%. This work demonstrates the potential of integrating LLM with multi-agent systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Planning with Multi-Constraints (PMC) is a new way to help artificial agents, like Google’s LLaMA, make decisions and take actions. Right now, these agents are really good at answering questions and having conversations, but they can struggle when it comes to making plans and following rules. PMC helps by breaking down big tasks into smaller ones that the agent can understand and follow. This makes the agent better at planning and working with other agents. In tests, PMC did a lot better than some other methods on two special challenges. |
Keywords
» Artificial intelligence » Gpt » Large language model » Llama » Zero shot
