Enhancing Embodied Decision-Making with Language Models: Leveraging Information Gathering through Proactive Questioning

Core Concepts

Language models can enhance embodied decision-making by actively gathering pertinent information through proactive questioning, leading to improved performance and efficiency compared to traditional approaches.

Abstract

The paper introduces a novel framework called "Contextual MDP with Human / External Information Sources in the Loop" that integrates information querying into the decision-making process. It then proposes a method called "Asking Before Acting" (ABA) that empowers language model agents to proactively inquire with external sources for relevant information using natural language during their interactions within the environment.
The key highlights and insights are:

When deployed in unfamiliar environments, language model agents can encounter challenges in efficiently gathering essential information, leading to suboptimal performance. In contrast, humans often seek additional information from peers prior to taking action.

ABA allows the agent to enhance its efficiency and performance by circumventing potentially laborious steps and combating the difficulties associated with exploration in unfamiliar environments and vagueness of instructions.

Extensive experiments are conducted across a spectrum of environments, including text-based household tasks, robot arm manipulation tasks, and real-world open-domain image-based tasks. The results demonstrate that ABA exhibits substantial advantages in both performance and efficiency over baseline language model agents.

Further finetuning ABA with reformulated metadata (ABA-FT) facilitates learning the rationale for asking and allows for additional enhancements, especially in tasks where baselines struggle.

The qualitative results on real-world open-domain tasks showcase the practical value of ABA and suggest a promising direction for future research.

Stats

"With strong capabilities of reasoning and a broad understanding of the world, Large Language Models (LLMs) have demonstrated immense potential in building versatile embodied decision-making agents capable of executing a wide array of tasks."
"Nevertheless, when deployed in unfamiliar environments, we show that LLM agents encounter challenges in efficiently gathering essential information, leading to suboptimal performance."
"Conversely, human individuals often seek additional information from their peers prior to taking action, harnessing external knowledge to avoid unnecessary trial and error."

Quotes

"Drawing inspiration from this behavior, we propose Asking Before Acting (ABA), a method that empowers the agent to proactively inquire with external sources for pertinent information using natural language during their interactions within the environment."
"The experiments involve various models from Vicuna to GPT-4. The results demonstrate that, even with modest prompts modifications, ABA exhibits substantial advantages on both performance and efficiency over baseline LLM agents."
"Further finetuning ABA with reformulated metadata (ABA-FT) faciliates learning the rationale for asking and allows for additional enhancements especially in tasks that baselines struggle to solve."

Key Insights Distilled From

Asking Before Acting: Gather Information in Embodied Decision Making with Language Models

by Xiaoyu Chen,... at arxiv.org 04-17-2024

https://arxiv.org/pdf/2305.15695.pdf

Deeper Inquiries

How can the proposed ABA framework be extended to handle more complex and dynamic environments, such as those with changing contexts or multiple human information sources

To extend the ABA framework to handle more complex and dynamic environments, several enhancements can be considered:

Adaptability to Changing Contexts: Implement a mechanism within the ABA framework to dynamically update the context information based on changes in the environment. This could involve continuous monitoring of the environment and adjusting the information querying strategy accordingly.

Integration of Multiple Information Sources: Modify the ABA framework to accommodate multiple human information sources. This could involve developing a hierarchical or collaborative querying system where the agent can interact with and gather information from multiple sources simultaneously.

Contextual Memory and Learning: Incorporate a contextual memory module within the ABA framework to store and retrieve relevant information from past interactions. This would enable the agent to leverage historical context to make informed decisions in dynamic environments.

Adaptive Questioning Strategies: Develop adaptive questioning strategies that can prioritize information sources based on their reliability or relevance in specific situations. This would allow the agent to optimize its information gathering process in complex environments.

Dynamic Task Planning: Integrate dynamic task planning capabilities into the ABA framework to enable the agent to adjust its actions based on real-time information gathered from human sources. This would enhance the agent's ability to navigate and make decisions in rapidly changing environments.

What are the potential limitations or drawbacks of the ABA approach, and how can they be addressed to further improve its performance and applicability

While the ABA approach shows promise in enhancing embodied decision-making with language models, there are potential limitations and drawbacks that need to be addressed:

Over-reliance on Human Input: A potential drawback of ABA is the reliance on human input for information gathering. This could introduce biases or inaccuracies in the information provided, impacting the agent's decision-making process. To address this, the framework could incorporate mechanisms for verifying and cross-referencing information from multiple sources.

Scalability and Generalization: A challenge for ABA is scalability and generalization to diverse environments. As the complexity of the environment increases, the framework may struggle to adapt and perform effectively. To improve scalability, the framework could be augmented with meta-learning techniques to enable rapid adaptation to new environments.

Complexity of Natural Language Understanding: Understanding and processing natural language queries and responses can be complex, especially in dynamic environments with multiple sources. Enhancements in natural language processing capabilities and semantic understanding could help mitigate this challenge.

Efficiency and Speed: The efficiency of the information gathering process in ABA could be improved to reduce the time taken to make decisions. Optimizing the questioning strategies and response processing mechanisms could enhance the speed of decision-making in dynamic environments.

Robustness to Noise and Uncertainty: ABA may face challenges in noisy or uncertain environments where human responses are ambiguous or conflicting. Developing robust mechanisms for handling uncertainty and noise in human-provided information is essential to improve the reliability of the decision-making process.

Given the promising results in embodied decision-making, how can the insights from this work be applied to other domains, such as interactive task planning or human-AI collaboration, to enhance the capabilities of language model-based systems

The insights from the work on embodied decision-making with the ABA framework can be applied to other domains to enhance the capabilities of language model-based systems:

Interactive Task Planning: The principles of proactive information gathering and human-in-the-loop decision-making can be applied to interactive task planning systems. By integrating language models with real-time feedback from users, systems can adapt and optimize task planning strategies based on user preferences and feedback.

Human-AI Collaboration: The collaborative nature of the ABA framework can be leveraged to enhance human-AI collaboration in various domains. By enabling AI systems to actively seek clarification and guidance from human experts, collaborative decision-making processes can be streamlined and optimized.

Personalized Assistance Systems: The concept of asking before acting can be utilized in personalized assistance systems to improve user interactions. By incorporating natural language querying and adaptive decision-making based on user preferences, these systems can provide tailored recommendations and assistance to users in various contexts.

Crisis Response and Emergency Management: In high-stakes environments such as crisis response and emergency management, language model-based systems with proactive information gathering capabilities can assist in decision-making processes. By actively seeking relevant information from multiple sources, these systems can support rapid and informed decision-making in critical situations.