Devil's Advocate: Anticipatory Reflection for LLM Agents
- URL: http://arxiv.org/abs/2405.16334v4
- Date: Thu, 20 Jun 2024 19:41:48 GMT
- Title: Devil's Advocate: Anticipatory Reflection for LLM Agents
- Authors: Haoyu Wang, Tao Li, Zhiwei Deng, Dan Roth, Yang Li,
- Abstract summary: Our approach prompts LLM agents to decompose a given task into manageable subtasks.
We implement a three-fold introspective intervention:.
Anticipatory reflection on potential failures and alternative remedy before action execution.
Post-action alignment with subtask objectives and backtracking with remedy to ensure utmost effort in plan execution.
- Score: 53.897557605550325
- License: http://creativecommons.org/licenses/by/4.0/
- 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. %; and when necessary, to explore ``the road not taken.'' 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 with a success rate of 23.5% over existing zero-shot methods by 3.5%. 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 by 45% needed to achieve a task.
Related papers
- InferAct: Inferring Safe Actions for LLM-Based Agents Through Preemptive Evaluation and Human Feedback [70.54226917774933]
This paper introduces InferAct, a novel approach that proactively detects potential errors before critical actions are executed.
InferAct is also capable of integrating human feedback to prevent irreversible risks and enhance the actor agent's decision-making process.
arXiv Detail & Related papers (2024-07-16T15:24:44Z) - Watch Every Step! LLM Agent Learning via Iterative Step-Level Process Refinement [50.481380478458945]
Iterative step-level Process Refinement (IPR) framework provides detailed step-by-step guidance to enhance agent training.
Our experiments on three complex agent tasks demonstrate that our framework outperforms a variety of strong baselines.
arXiv Detail & Related papers (2024-06-17T03:29:13Z) - Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents [49.85633804913796]
We present an exploration-based trajectory optimization approach, referred to as ETO.
This learning method is designed to enhance the performance of open LLM agents.
Our experiments on three complex tasks demonstrate that ETO consistently surpasses baseline performance by a large margin.
arXiv Detail & Related papers (2024-03-04T21:50:29Z) - AdaPlanner: Adaptive Planning from Feedback with Language Models [56.367020818139665]
Large language models (LLMs) have recently demonstrated the potential in acting as autonomous agents for sequential decision-making tasks.
We propose a closed-loop approach, AdaPlanner, which allows the LLM agent to refine its self-generated plan adaptively in response to environmental feedback.
To mitigate hallucination, we develop a code-style LLM prompt structure that facilitates plan generation across a variety of tasks, environments, and agent capabilities.
arXiv Detail & Related papers (2023-05-26T05:52:27Z) - Task-Agnostic Continual Reinforcement Learning: Gaining Insights and
Overcoming Challenges [27.474011433615317]
Continual learning (CL) enables the development of models and agents that learn from a sequence of tasks.
We investigate the factors that contribute to the performance differences between task-agnostic CL and multi-task (MTL) agents.
arXiv Detail & Related papers (2022-05-28T17:59:00Z) - Model-based Adversarial Meta-Reinforcement Learning [38.28304764312512]
We propose Model-based Adversarial Meta-Reinforcement Learning (AdMRL)
AdMRL aims to minimize the worst-case sub-optimality gap across all tasks in a family of tasks.
We evaluate our approach on several continuous control benchmarks and demonstrate its efficacy in the worst-case performance over all tasks.
arXiv Detail & Related papers (2020-06-16T02:21:49Z) - Planning to Explore via Self-Supervised World Models [120.31359262226758]
Plan2Explore is a self-supervised reinforcement learning agent.
We present a new approach to self-supervised exploration and fast adaptation to new tasks.
Without any training supervision or task-specific interaction, Plan2Explore outperforms prior self-supervised exploration methods.
arXiv Detail & Related papers (2020-05-12T17:59:45Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.