Enhancing LLM Agents for Open-World Interactive Tasks through Weak Exploration and Strong Exploitation

Core Concepts

To enhance LLM agents in solving open-world interactive tasks, this paper proposes a novel approach called Weak Exploration to Strong Exploitation (WESE). WESE decouples the exploration and exploitation processes, employing a cost-effective weak agent for exploration and leveraging the acquired knowledge to empower a stronger agent for exploitation.

Abstract

This paper introduces a novel approach called Weak Exploration to Strong Exploitation (WESE) to enhance LLM agents in solving open-world interactive tasks. The key ideas are:

Decoupling exploration and exploitation: WESE constructs two distinct LLM agents, a weak agent for exploration and a strong agent for exploitation. The weak agent interacts with the environment to acquire global knowledge, while the strong agent leverages this knowledge for decision-making and task completion.

Knowledge compression and retrieval: The environmental information obtained during exploration is structured into a knowledge graph. During exploitation, a one-hop retrieval strategy is used to extract task-relevant knowledge from the graph, reducing the interference of irrelevant information.

Weak exploration to strong exploitation: A weaker LLM (e.g., Llama-2-7B) is used as the exploration agent to reduce resource consumption, while the stronger LLM (e.g., text-davinci-003) is employed for the exploitation task.

The authors evaluate WESE on four open-world interactive benchmarks, including decision-making tasks (ALFWorld, ScienceWorld) and question-answering tasks (HotPotQA, FEVER). The results demonstrate that WESE achieves a remarkable balance between effectiveness, efficiency, and cost, outperforming baseline methods.

Stats

The agent needs to interact with the environment to acquire information for solving complex tasks, which can involve more than 50 steps and 50 locations.
In the HotPotQA and FEVER tasks, the agent needs to search for relevant information on Wikipedia to answer the questions.

Quotes

"Existing prompt-engineering approaches primarily consider two factors: how to incorporate task-relevant information in the prompt, and how to elicit the reasoning ability of LLMs through prompts."
"Exploration involves perceiving the environment and obtaining task-relevant information, while exploitation involves making action decisions based on existing knowledge."

Key Insights Distilled From

WESE

by Xu Huang,Wei... at arxiv.org 04-12-2024

https://arxiv.org/pdf/2404.07456.pdf

Deeper Inquiries

How can the knowledge graph be further enhanced to capture more comprehensive and structured information from the environment?

To enhance the knowledge graph's capability to capture more comprehensive and structured information from the environment, several strategies can be implemented:

Entity Linking and Disambiguation: Implementing entity linking techniques to identify and disambiguate entities mentioned in the environment feedback can improve the accuracy and relevance of the information stored in the knowledge graph.

Relation Extraction: Incorporating relation extraction methods to identify and extract relationships between entities mentioned in the environment feedback can enrich the knowledge graph with structured information.

Contextual Understanding: Enhancing the knowledge graph with contextual understanding capabilities can help in capturing the nuanced relationships and dependencies between entities and information obtained during exploration.

Temporal and Spatial Context: Incorporating temporal and spatial context into the knowledge graph can provide a more comprehensive view of the environment, enabling the agent to make more informed decisions based on historical and spatial information.

Hierarchical Structure: Organizing the knowledge graph in a hierarchical structure can help in representing complex relationships and dependencies in a more structured and organized manner, facilitating efficient retrieval of task-relevant information during exploitation.

By implementing these enhancements, the knowledge graph can evolve into a powerful tool for storing, organizing, and retrieving comprehensive and structured information from the environment, thereby enhancing the overall performance of the WESE framework.

How can the potential limitations of the one-hop retrieval strategy be addressed, and how can it be improved to handle more complex tasks?

The one-hop retrieval strategy, while effective in filtering out irrelevant information and extracting task-relevant knowledge from the knowledge graph, may have limitations when handling more complex tasks. To address these limitations and improve the strategy for handling complex tasks, the following approaches can be considered:

Multi-hop Retrieval: Implementing a multi-hop retrieval mechanism that allows the agent to traverse multiple layers of the knowledge graph to capture deeper and more intricate relationships between entities can enhance the strategy's ability to handle complex tasks that require a deeper level of understanding.

Semantic Similarity: Incorporating semantic similarity measures to compare task-related entities with the information stored in the knowledge graph can improve the relevance and accuracy of the retrieved knowledge, enabling the agent to make more informed decisions during exploitation.

Dynamic Graph Updating: Implementing a dynamic graph updating mechanism that continuously updates the knowledge graph based on new information acquired during exploration can ensure the relevance and freshness of the stored information, especially in dynamic and evolving environments.

Attention Mechanisms: Integrating attention mechanisms into the retrieval process can help the agent focus on the most relevant and informative parts of the knowledge graph, improving the efficiency and effectiveness of the retrieval process for complex tasks.

By incorporating these enhancements and addressing the potential limitations of the one-hop retrieval strategy, the WESE framework can be better equipped to handle more complex tasks and provide more accurate and relevant information during the exploitation phase.

Can the WESE framework be extended to other types of open-world tasks, such as those involving visual or multimodal information?

Yes, the WESE framework can be extended to other types of open-world tasks, including those involving visual or multimodal information. To adapt the WESE framework for tasks that incorporate visual or multimodal elements, the following modifications and enhancements can be considered:

Integration of Vision Models: Incorporating vision models or multimodal models alongside language models can enable the agent to interact with visual information in the environment, expanding the scope of exploration and exploitation to include visual cues and feedback.

Multimodal Knowledge Graph: Developing a multimodal knowledge graph that integrates information from both textual and visual sources can provide a more comprehensive and holistic view of the environment, enabling the agent to leverage both types of information for task-solving.

Cross-Modal Retrieval: Implementing cross-modal retrieval mechanisms that allow the agent to retrieve relevant information from both textual and visual modalities can enhance the exploitation process by leveraging the complementary nature of different modalities.

Interactive Exploration: Enabling interactive exploration mechanisms that allow the agent to interact with and manipulate visual elements in the environment can enhance the exploration phase, enabling the agent to gather visual information and feedback for task resolution.

By extending the WESE framework to incorporate visual and multimodal information, the agent can effectively tackle a wider range of open-world tasks that involve diverse types of information, leading to more robust and versatile intelligent agent capabilities.