Devil's Advocate: Anticipatory Reflection for LLM Agents
CoRR(2024)
摘要
In this work, we introduce a novel approach that equips LLM agents with
introspection, enhancing consistency and adaptability in solving complex tasks.
Our approach prompts LLM agents to decompose a given task into manageable
subtasks (i.e., to make a plan), and to continuously introspect upon the
suitability and results of their actions. We implement a three-fold
introspective intervention: 1) anticipatory reflection on potential failures
and alternative remedy before action execution, 2) post-action alignment with
subtask objectives and backtracking with remedy to ensure utmost effort in plan
execution, and 3) comprehensive review upon plan completion for future strategy
refinement. By deploying and experimenting with this methodology - a zero-shot
approach - within WebArena for practical tasks in web environments, our agent
demonstrates superior performance over existing zero-shot methods. The
experimental results suggest that our introspection-driven approach not only
enhances the agent's ability to navigate unanticipated challenges through a
robust mechanism of plan execution, but also improves efficiency by reducing
the number of trials and plan revisions needed to achieve a task.
更多查看译文
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要