Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
41 tokens/sec
GPT-4o
59 tokens/sec
Gemini 2.5 Pro Pro
41 tokens/sec
o3 Pro
7 tokens/sec
GPT-4.1 Pro
50 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Devil's Advocate: Anticipatory Reflection for LLM Agents (2405.16334v4)

Published 25 May 2024 in cs.AI

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.

PDF HTML Abstract

Enhancing Decision-Making in LLM Agents with Introspective Capabilities

The paper, "Devil's Advocate: Anticipatory Reflection for LLM Agents," presents a new methodology that integrates introspection into the functionality of LLM agents. This method aims to improve these agents' ability to handle complex tasks with enhanced consistency and adaptability.

Overview of Methodology

The paper introduces a structured approach to augment the decision-making competence of LLM agents through anticipatory reflection, post-action evaluation, and plan revision. This involves a three-tiered introspection mechanism:

  1. Anticipatory Reflection: Before executing any action, the LLM agent pre-empts potential failures by formulating alternative actions or "remedies." This method functions akin to a devil's advocate, allowing the agent to foresee possible errors and prepare corrective measures preemptively.
  2. Post-Action Evaluation: After each action is executed, an assessment is conducted to gauge alignment with subtask objectives. If deviations from the desired outcome are detected, the agent can backtrack and explore alternative remedies, improving reliability and reducing repeated errors.
  3. Comprehensive Plan Revision: Upon task failure or completion, the agent reviews the trajectory of actions, reflecting on inefficiencies, to refine future operational strategies.

Experimental Evaluation

The implementation of this introspective approach was tested within WebArena, a virtual web environment tailored to emulate practical decision-making tasks. Evaluation metrics included success rates and efficiency improvements in task completion compared to existing methods.

The examination revealed that the proposed approach achieved a notable success rate of 23.5%, surpassing existing zero-shot methods by 3.5%. Additionally, the introspection-driven approach significantly reduced the need for trial iterations and plan revisions by 45%, indicating enhanced operational efficiency. These results demonstrate the system's refined capability to navigate and adapt to complex, dynamic web-based tasks.

Implications and Future Directions

The integration of introspective mechanisms in LLM agents marks progress toward more autonomous and cognitively flexible AI systems. Practically, it suggests promising applications in sectors requiring real-time decision-making such as digital assistant technologies, process automation, and complex data querying operations.

Theoretically, this paper opens avenues for further exploration into enhancing LLM agents' cognitive architectures. Future research could explore optimizing the types and layers of reflective queries, expanding capabilities for handling higher degrees of task complexity, and exploring the scalability of introspective LLM models.

Moreover, adapting the introspective framework to support learning from multimodal data inputs—such as combining textual inputs with visual data—could amplify the contextual understanding and decision accuracy of LLM agents in more diverse and dynamic environments.

In summary, this paper's contribution to equipping LLM agents with introspective capabilities lays a foundational step in evolving machine learning models from mere automated processors into more sophisticated, adaptive systems with human-like decision-making agility.

File Document Download Save Streamline Icon: https://streamlinehq.com PDF File Document Download Save Streamline Icon: https://streamlinehq.com Markdown Bookmark Chat Bubble Oval Streamline Icon: https://streamlinehq.com Chat (Pro)
Definition Search Book Streamline Icon: https://streamlinehq.com
References (34)
  1. Gpt-4 technical report. arXiv preprint arXiv:2303.08774.
  2. Mind2Web: Towards a Generalist Agent for the Web.
  3. PaLM-E: An Embodied Multimodal Language Model. In arXiv preprint arXiv:2303.03378.
  4. Zero-Shot On-the-Fly Event Schema Induction. In Findings of the Association for Computational Linguistics: EACL 2023, pages 705–725, Dubrovnik, Croatia. Association for Computational Linguistics.
  5. Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning.
  6. A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis. In The Twelfth International Conference on Learning Representations.
  7. Reasoning with Language Model is Planning with World Model. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 8154–8173, Singapore. Association for Computational Linguistics.
  8. Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents. arXiv preprint arXiv:2201.07207.
  9. Inner Monologue: Embodied Reasoning through Planning with Language Models. In arXiv preprint arXiv:2207.05608.
  10. A Zero-Shot Language Agent for Computer Control with Structured Reflection. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 11261–11274, Singapore. Association for Computational Linguistics.
  11. Reinforcement learning on web interfaces using workflow-guided exploration. arXiv preprint arXiv:1802.08802.
  12. AgentBench: Evaluating LLMs as Agents. arXiv preprint arXiv: 2308.03688.
  13. Self-Refine: Iterative Refinement with Self-Feedback.
  14. Autonomous Evaluation and Refinement of Digital Agents.
  15. ADaPT: As-Needed Decomposition and Planning with Language Models. arXiv.
  16. Reflexion: Language Agents with Verbal Reinforcement Learning.
  17. ALFWorld: Aligning Text and Embodied Environments for Interactive Learning. In Proceedings of the International Conference on Learning Representations (ICLR).
  18. LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV).
  19. Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents. arXiv preprint arXiv:2403.02502.
  20. AdaPlanner: Adaptive Planning from Feedback with Language Models.
  21. Voyager: An Open-Ended Embodied Agent with Large Language Models. arXiv preprint arXiv: Arxiv-2305.16291.
  22. Large Language Models are not Fair Evaluators.
  23. ScienceWorld: Is your Agent Smarter than a 5th Grader? In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 11279–11298, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
  24. Describe, Explain, Plan and Select: Interactive Planning with LLMs Enables Open-World Multi-Task Agents. In Thirty-seventh Conference on Neural Information Processing Systems.
  25. Embodied Task Planning with Large Language Models. arXiv preprint arXiv:2305.03716.
  26. ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language Models.
  27. WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents. In ArXiv.
  28. Tree of Thoughts: Deliberate Problem Solving with Large Language Models. In Thirty-seventh Conference on Neural Information Processing Systems.
  29. ReAct: Synergizing Reasoning and Acting in Language Models. In International Conference on Learning Representations (ICLR).
  30. Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena. In Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track.
  31. Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models.
  32. WebArena: A Realistic Web Environment for Building Autonomous Agents. In The Twelfth International Conference on Learning Representations.
  33. Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and Memory. arXiv preprint arXiv:2305.17144.
  34. ToolChain*: Efficient Action Space Navigation in Large Language Models with A* Search. In The Twelfth International Conference on Learning Representations.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (5)
  1. Haoyu Wang (309 papers)
  2. Tao Li (440 papers)
  3. Zhiwei Deng (33 papers)
  4. Dan Roth (222 papers)
  5. Yang Li (1140 papers)
Citations (2)
View on Semantic Scholar
Youtube Logo Streamline Icon: https://streamlinehq.com

YouTube