Devil’s Advocate:Anticipatory Reflection for LLM Agents
Abstract
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 failure 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.
Deploying this methodology within WebArena for practical tasks in web environments, our agent demonstrates superior performance over existing zero-shot methods.
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 reflection and plan revision.
We implement a three-fold introspective intervention:
1) anticipatory reflection on potential failure 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.
Deploying this methodology within WebArena for practical tasks in web environments, our agent demonstrates superior performance over existing zero-shot methods.
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 reflection and plan revision.
