Summary of Ask-before-plan: Proactive Language Agents For Real-world Planning, by Xuan Zhang et al.
Ask-before-Plan: Proactive Language Agents for Real-World Planning
by Xuan Zhang, Yang Deng, Zifeng Ren, See-Kiong Ng, Tat-Seng Chua
First submitted to arxiv on: 18 Jun 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 The paper introduces a new task called Proactive Agent Planning, which requires language agents to predict clarification needs based on user-agent conversation and agent-environment interaction, invoke external tools to collect valid information, and generate a plan to fulfill the user’s demands. The authors propose a novel multi-agent framework called Clarification-Execution-Planning (CEP) to tackle the deficiency of Large Language Models (LLMs) in proactive planning. CEP consists of three agents specialized in clarification, execution, and planning. The authors also introduce the trajectory tuning scheme for the clarification agent and static execution agent, as well as the memory recollection mechanism for the dynamic execution agent. Extensive evaluations conducted on the Ask-before-Plan dataset validate the effectiveness of the proposed framework. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about how computers can understand what people are asking them to do better. It introduces a new way for computers to plan and make decisions, called Proactive Agent Planning. This involves predicting when someone will need more information, getting that information, and then making a plan to complete the task. The authors propose a new system that does this well, using three special agents that work together. They also introduce some new techniques to help these agents do their job better. |
