AutoGuide: Bridging Knowledge Gaps for LLM Agents with State-Aware Guidelines

Concepts de base

AutoGuide bridges knowledge gaps in pre-trained LLMs by extracting state-aware guidelines from offline experiences, enhancing decision-making.

Résumé

AutoGuide introduces a framework to extract state-aware guidelines from offline data, improving LLM agents' decision-making. By leveraging implicit knowledge in offline experiences, AutoGuide provides concise natural language guidelines that enhance an agent's performance. The method outperforms competitive baselines in sequential decision-making benchmarks by providing relevant guidelines at test time based on the current state.

Stats

AutoGuide outperforms competitive LLM-based baselines by a large margin in sequential decision-making benchmarks.
AutoGuide achieves the highest success rates compared to competitive baselines in challenging sequential decision-making benchmark environments.
AutoGuide generates state-aware guidelines in concise natural language statements, efficiently compressing knowledge in offline data.

Citations

Idées clés tirées de

AutoGuide

by Yao Fu,Dong-... à arxiv.org 03-15-2024

https://arxiv.org/pdf/2403.08978.pdf

Questions plus approfondies

How does AutoGuide adapt to extreme types of offline data with only successful or failed trajectories?

AutoGuide is designed to be flexible and robust in handling different types of offline data, including scenarios where only successful or failed trajectories are available. In cases where the offline data consists solely of successful instances, AutoGuide samples multiple trajectories and prompts a language model to identify states that exhibit common behaviors across them. It then utilizes these identified states to generate state-aware guidelines by summarizing them into concise natural language statements. On the other hand, when dealing with failed trajectories exclusively, AutoGuide leverages these failures along with a one-shot example from ReAct as a successful demonstration to extract valuable knowledge from the offline experiences.

Can combining AutoGuide with test-time self-feedback approaches like Reflexion further enhance performance?

Combining AutoGuide with test-time self-feedback approaches like Reflexion can indeed lead to enhanced performance in decision-making tasks. While AutoGuide focuses on extracting domain knowledge from offline experiences and providing relevant state-aware guidelines for LLM agents during testing, Reflexion offers intra-task feedback based on environmental responses during inference. By integrating both inter-task knowledge derived from extracted guidelines and intra-task feedback provided by Reflexion, LLM agents can benefit from a comprehensive set of information that aids in accurate action selection and improved task completion rates.

What are the contributions of each component within AutoGuide to its overall effectiveness?

State Summarization Module (SS): The State Summarization module plays a crucial role in generating concise descriptions of states at specific timesteps by contrasting successful and failed trajectories. This step ensures that relevant context is captured for effective decision-making.

Guideline Extraction Module (GES): The Guideline Extraction module extracts desired guidelines corresponding to identified states by analyzing patterns between success and failure trajectories. These extracted guidelines provide actionable insights for LLM agents during inference.

Integration: By integrating both state summaries and extracted guidelines into the agent's prompt at test time, AutoGuide enables precise action selection based on pertinent domain knowledge associated with each encountered state. This integration enhances an agent's decision-making process significantly compared to baselines without such tailored guidance mechanisms.