Embodied Question Answering via Multi-LLM Systems

Bhrij Patel, Vishnu Sashank Dorbala, Amrit Singh Bedi·June 16, 2024

Summary

This paper investigates the use of multiple large language models (LLMs) in a multi-agent framework for Embodied Question Answering (EQA) in household environments. The Central Answer Model (CAM) is introduced, which aggregates individual LLM responses, resulting in a 50% higher accuracy compared to ensemble methods like voting. CAM eliminates agent communication, improving efficiency. The study compares different aggregation algorithms, analyzes feature importance, and integrates with a state-of-the-art exploration system. It showcases the potential of multi-agent systems to enhance exploration and answer accuracy, with a focus on zero-shot performance and leveraging LLMs for more realistic AI agents. Future work includes addressing limitations, expanding to complex questions, and adapting to evolving scenarios.

Key findings

4

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the problem of Embodied Question Answering (EQA) using a novel Multi-LLM agent approach, specifically focusing on dynamic household environments . This problem involves training a Central Answer Model (CAM) to aggregate responses from multiple agents to predict answers to binary embodied questions about the household without the need for communication between agents . While EQA itself is not a new problem, the approach proposed in the paper, utilizing Multi-LLM agents and a CAM for aggregation, presents a novel solution to enhance the accuracy and efficiency of answering questions in dynamic environments .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to Embodied Question Answering (EQA) using a novel Multi-LLM agent approach. The hypothesis focuses on addressing the challenges in EQA by training a Central Answer Model (CAM) on the answers provided by independent agents without the need for communication between agents. The goal is to improve the accuracy of answering binary embodied questions about the household by aggregating responses from multiple agents through the CAM classifier .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper proposes several novel ideas, methods, and models in the field of Embodied Question Answering (EQA) using a Multi-LLM (Large Language Model) approach . Here are the key contributions outlined in the paper:

  1. Central Answer Model (CAM):

    • The paper introduces a Central Answer Model (CAM) for Embodied Question Answering in a Multi-Agent setting. CAM acts as a classifier that aggregates responses from multiple LLM-based agents to predict an answer .
    • CAM is trained on labeled query datasets using various machine learning methods and has shown up to 50% higher accuracy compared to traditional majority vote and debate aggregation methods .
  2. Integration with Exploration Systems:

    • The paper evaluates the Multi-LLM framework by incorporating data gathered through a State-of-the-Art (SOTA) exploration method. This method involves LLM-based agents exploring a household environment before answering questions .
    • The exploration phase includes Language-Guided Exploration (LGX) where agents navigate the house graph to observe different items, enhancing the system's ability to answer questions based on observations .
  3. Novel Contributions:

    • The paper's novel contributions include addressing the challenges of EQA using a Multi-LLM agent approach, training a CAM for answer aggregation, and reducing the need for communication between agents .
    • The proposed framework aims to improve the accuracy and efficiency of EQA tasks by leveraging multiple LLM-based agents and a central classifier for answer prediction .

Overall, the paper introduces innovative approaches to enhance EQA tasks by leveraging Multi-LLM systems, CAM for answer aggregation, and integration with exploration systems to improve the accuracy and practicality of Embodied Question Answering . The paper introduces a novel Multi-LLM framework for Embodied Question Answering (EQA) that offers several key characteristics and advantages compared to previous methods . Here are the detailed characteristics and advantages outlined in the paper:

  1. Central Answer Model (CAM):

    • The proposed Central Answer Model (CAM) acts as a classifier that aggregates responses from multiple LLM-based agents to predict an answer, eliminating the need for communication between agents during inference .
    • CAM outperforms traditional aggregation methods like majority voting and multi-LLM debating, achieving up to 50% higher accuracy in EQA tasks .
  2. Integration with Exploration Systems:

    • The Multi-LLM framework integrates with State-of-the-Art (SOTA) exploration methods, where LLM-based agents explore household environments before answering questions, enhancing the system's practicality in real-world scenarios .
    • By incorporating Language-Guided Exploration (LGX), the system allows agents to navigate the house graph, observe different items, and improve their ability to answer questions based on observations .
  3. Feature Importance Analysis:

    • The paper conducts feature importance analysis to evaluate the impact of input features on the model's final answer in EQA tasks .
    • This analysis helps in understanding how the model learns to weigh responses from different agents based on observations, leading to more accurate and reliable answers .
  4. Advantages Over Previous Methods:

    • The Multi-LLM framework offers improved accuracy in EQA tasks by leveraging multiple LLM-based agents and a central classifier for answer prediction, surpassing traditional aggregation methods .
    • The system's ability to work without communication during inference reduces time costs and enhances efficiency in answering embodied questions .

In conclusion, the Multi-LLM framework presented in the paper demonstrates superior performance, practicality, and efficiency in Embodied Question Answering tasks compared to previous methods, showcasing advancements in the field of AI research .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies exist in the field of embodied question answering via Multi-LLM systems. Noteworthy researchers in this area include Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Shaokun Zhang, Erkang Zhu, Beibin Li, Li Jiang, Xiaoyun Zhang, Chi Wang, Mikael Henaff, Sneha Silwal, Paul Mcvay, Oleksandr Maksymets, Sergio Arnaud, Karmesh Yadav, Qiyang Li, Ben Newman, Mohit Sharma, Vincent Berges, Shiqi Zhang, Pulkit Agrawal, Yonatan Bisk, Dhruv Batra, Mrinal Kalakrishnan, Franziska Meier, Chris Paxton, Sasha Sax, Aravind Rajeswaran, and many others .

The key to the solution mentioned in the paper involves adapting the Multi-LLM setup for Embodied Question Answering (EQA) in dynamic household environments, exploring aggregation methods for situational, subjective, and non-binary questions, and applying the CAM aggregation method to question-answering tasks beyond Embodied AI, such as long video understanding. Additionally, the quality of observation data, relying on the GLIP model, plays a crucial role in the results obtained .


How were the experiments in the paper designed?

The experiments in the paper were designed with a structured approach:

  • The experiments involved evaluating a Multi-LLM framework by gathering observations using a state-of-the-art exploration method .
  • The experiments followed a setup where observations were collected and stored by specific agents during an exploration phase, and the answers from the LLM were stored offline .
  • The experimentation process included using a 95%-5% random train-test split over 5 seeds and performing experiments in two different environments with varying numbers of nodes .
  • The experiments aimed to measure the accuracy of CAM for Embodied Question Answering (EQA) and demonstrate its performance over other aggregation baselines .
  • The experiments involved comparing the performance of various CAM methods and baselines in different graph environments, showcasing the superiority of CAM methods in terms of accuracy .
  • The experiments also included a feature importance analysis to understand how input impacts the model's final answer in the Multi-LLM system .
  • The experiments were structured to evaluate the inference time accuracy of the model and compare the performance of CAM against baseline aggregation approaches using various machine learning algorithms .
  • The experiments were designed to highlight the benefits of the Multi-LLM system, the practicality of using CAM for EQA, and the efficiency of the system in real-world scenarios .

What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is not explicitly mentioned in the provided contexts. However, the study focuses on improving factual-ity and reasoning in language models through multi-agent debate . Regarding the open-source code, the information about the code being open source is not provided in the contexts. It is recommended to refer directly to the study or contact the authors for specific details on the dataset and the availability of the code .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that needed verification. The study conducted a comprehensive analysis of a Multi-LLM framework for Embodied Question Answering (EQA) in dynamic household environments . The experiments involved evaluating the performance of the CAM model against baselines in different Matterport environments, demonstrating the superiority of the CAM model in terms of accuracy during test-time inference . This analysis showcases the effectiveness of the Multi-LLM system in real-world scenarios, emphasizing its practicality and performance .

Furthermore, the study delved into feature importance analysis within the Multi-LLM system, which is a crucial metric to understand how input impacts the model's final answer . By providing insights into the feature importance of the system, the research contributes to a deeper understanding of the functioning and decision-making processes of the Multi-LLM framework in EQA tasks .

Overall, the experiments conducted in the paper, along with the results obtained, offer strong empirical evidence to support the scientific hypotheses under investigation. The detailed analysis of the Multi-LLM framework's performance, feature importance, and practical application in dynamic environments contributes significantly to the advancement of research in Embodied Question Answering .


What are the contributions of this paper?

The contributions of this paper include:

  • Tackling Embodied EQA with LLM-based Multi-agent systems .
  • Providing a feature importance analysis on the framework .
  • Introducing a central classifier trained on independent answers from multiple agents to eliminate the need for communication .

What work can be continued in depth?

To further advance the research in this area, several avenues can be explored based on the existing work:

  • Adapting the Multi-LLM setup for Embodied Question Answering (EQA) in dynamic household environments where ground truth labels are challenging to obtain due to constantly changing non-stationary items .
  • Exploring aggregation methods for situational, subjective, and non-binary questions to enhance practicality in EQA tasks beyond binary "Yes/No" queries .
  • Analyzing the impact of different scene understanding algorithms on the quality of observation data in the Multi-LLM framework for EQA .
  • Extending the application of the CAM aggregation method to question-answering tasks outside of Embodied AI, such as long video understanding .
  • Investigating the potential of Multi-LLM systems for simultaneous exploration of household environments to improve efficiency in EQA tasks .
  • Addressing the challenge of conflicting responses from LLM-based agents in a multi-agent setup by exploring methods to reconcile varying responses .
  • Considering the removal of the need for communication by training a central classifier on independent answers from multiple agents to streamline the EQA process .

Tables

1

Introduction
Background
Evolution of embodied AI and EQA
Importance of multi-agent systems in complex environments
Objective
To develop and evaluate CAM for EQA
Improve accuracy and efficiency using LLMs
Focus on zero-shot performance and real-world applicability
Method
Data Collection
Selection of diverse household environment datasets
EQA tasks and question-answer pairs
Data Preprocessing
Cleaning and standardization of input data
Handling domain-specific language and ambiguity
Central Answer Model (CAM)
CAM Architecture
Integration of multiple LLMs
Aggregation techniques (e.g., weighted averaging, majority voting)
Performance Evaluation
Accuracy comparison with ensemble methods
Zero-shot and few-shot experiments
Feature Importance Analysis
Identifying key factors in LLM responses
Contribution to overall performance
Integration with Exploration System
State-of-the-art exploration strategies
Enhancing agent exploration and decision-making
Results and Analysis
CAM's accuracy boost over ensemble methods
Efficiency improvements through communication reduction
Effect on exploration and answer accuracy
Limitations and Future Work
Addressing current challenges
Complex question handling
Adapting to dynamic scenarios
Conclusion
The potential of LLMs in enhancing EQA systems
Implications for realistic AI agent development
Directions for future research in multi-agent embodied AI
Basic info
papers
computation and language
machine learning
artificial intelligence
Advanced features
Insights
What is the significance of integrating LLMs in multi-agent systems for EQA?
What are the potential future directions mentioned in the study?
How does the Central Answer Model (CAM) improve over ensemble methods in Embodied Question Answering?
What is the primary focus of the paper?

Embodied Question Answering via Multi-LLM Systems

Bhrij Patel, Vishnu Sashank Dorbala, Amrit Singh Bedi·June 16, 2024

Summary

This paper investigates the use of multiple large language models (LLMs) in a multi-agent framework for Embodied Question Answering (EQA) in household environments. The Central Answer Model (CAM) is introduced, which aggregates individual LLM responses, resulting in a 50% higher accuracy compared to ensemble methods like voting. CAM eliminates agent communication, improving efficiency. The study compares different aggregation algorithms, analyzes feature importance, and integrates with a state-of-the-art exploration system. It showcases the potential of multi-agent systems to enhance exploration and answer accuracy, with a focus on zero-shot performance and leveraging LLMs for more realistic AI agents. Future work includes addressing limitations, expanding to complex questions, and adapting to evolving scenarios.
Mind map
Contribution to overall performance
Identifying key factors in LLM responses
Adapting to dynamic scenarios
Complex question handling
Addressing current challenges
Enhancing agent exploration and decision-making
State-of-the-art exploration strategies
Feature Importance Analysis
Aggregation techniques (e.g., weighted averaging, majority voting)
Integration of multiple LLMs
Central Answer Model (CAM)
EQA tasks and question-answer pairs
Selection of diverse household environment datasets
Focus on zero-shot performance and real-world applicability
Improve accuracy and efficiency using LLMs
To develop and evaluate CAM for EQA
Importance of multi-agent systems in complex environments
Evolution of embodied AI and EQA
Directions for future research in multi-agent embodied AI
Implications for realistic AI agent development
The potential of LLMs in enhancing EQA systems
Limitations and Future Work
Integration with Exploration System
Performance Evaluation
CAM Architecture
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Results and Analysis
Method
Introduction
Outline
Introduction
Background
Evolution of embodied AI and EQA
Importance of multi-agent systems in complex environments
Objective
To develop and evaluate CAM for EQA
Improve accuracy and efficiency using LLMs
Focus on zero-shot performance and real-world applicability
Method
Data Collection
Selection of diverse household environment datasets
EQA tasks and question-answer pairs
Data Preprocessing
Cleaning and standardization of input data
Handling domain-specific language and ambiguity
Central Answer Model (CAM)
CAM Architecture
Integration of multiple LLMs
Aggregation techniques (e.g., weighted averaging, majority voting)
Performance Evaluation
Accuracy comparison with ensemble methods
Zero-shot and few-shot experiments
Feature Importance Analysis
Identifying key factors in LLM responses
Contribution to overall performance
Integration with Exploration System
State-of-the-art exploration strategies
Enhancing agent exploration and decision-making
Results and Analysis
CAM's accuracy boost over ensemble methods
Efficiency improvements through communication reduction
Effect on exploration and answer accuracy
Limitations and Future Work
Addressing current challenges
Complex question handling
Adapting to dynamic scenarios
Conclusion
The potential of LLMs in enhancing EQA systems
Implications for realistic AI agent development
Directions for future research in multi-agent embodied AI
Key findings
4

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the problem of Embodied Question Answering (EQA) using a novel Multi-LLM agent approach, specifically focusing on dynamic household environments . This problem involves training a Central Answer Model (CAM) to aggregate responses from multiple agents to predict answers to binary embodied questions about the household without the need for communication between agents . While EQA itself is not a new problem, the approach proposed in the paper, utilizing Multi-LLM agents and a CAM for aggregation, presents a novel solution to enhance the accuracy and efficiency of answering questions in dynamic environments .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to Embodied Question Answering (EQA) using a novel Multi-LLM agent approach. The hypothesis focuses on addressing the challenges in EQA by training a Central Answer Model (CAM) on the answers provided by independent agents without the need for communication between agents. The goal is to improve the accuracy of answering binary embodied questions about the household by aggregating responses from multiple agents through the CAM classifier .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper proposes several novel ideas, methods, and models in the field of Embodied Question Answering (EQA) using a Multi-LLM (Large Language Model) approach . Here are the key contributions outlined in the paper:

  1. Central Answer Model (CAM):

    • The paper introduces a Central Answer Model (CAM) for Embodied Question Answering in a Multi-Agent setting. CAM acts as a classifier that aggregates responses from multiple LLM-based agents to predict an answer .
    • CAM is trained on labeled query datasets using various machine learning methods and has shown up to 50% higher accuracy compared to traditional majority vote and debate aggregation methods .
  2. Integration with Exploration Systems:

    • The paper evaluates the Multi-LLM framework by incorporating data gathered through a State-of-the-Art (SOTA) exploration method. This method involves LLM-based agents exploring a household environment before answering questions .
    • The exploration phase includes Language-Guided Exploration (LGX) where agents navigate the house graph to observe different items, enhancing the system's ability to answer questions based on observations .
  3. Novel Contributions:

    • The paper's novel contributions include addressing the challenges of EQA using a Multi-LLM agent approach, training a CAM for answer aggregation, and reducing the need for communication between agents .
    • The proposed framework aims to improve the accuracy and efficiency of EQA tasks by leveraging multiple LLM-based agents and a central classifier for answer prediction .

Overall, the paper introduces innovative approaches to enhance EQA tasks by leveraging Multi-LLM systems, CAM for answer aggregation, and integration with exploration systems to improve the accuracy and practicality of Embodied Question Answering . The paper introduces a novel Multi-LLM framework for Embodied Question Answering (EQA) that offers several key characteristics and advantages compared to previous methods . Here are the detailed characteristics and advantages outlined in the paper:

  1. Central Answer Model (CAM):

    • The proposed Central Answer Model (CAM) acts as a classifier that aggregates responses from multiple LLM-based agents to predict an answer, eliminating the need for communication between agents during inference .
    • CAM outperforms traditional aggregation methods like majority voting and multi-LLM debating, achieving up to 50% higher accuracy in EQA tasks .
  2. Integration with Exploration Systems:

    • The Multi-LLM framework integrates with State-of-the-Art (SOTA) exploration methods, where LLM-based agents explore household environments before answering questions, enhancing the system's practicality in real-world scenarios .
    • By incorporating Language-Guided Exploration (LGX), the system allows agents to navigate the house graph, observe different items, and improve their ability to answer questions based on observations .
  3. Feature Importance Analysis:

    • The paper conducts feature importance analysis to evaluate the impact of input features on the model's final answer in EQA tasks .
    • This analysis helps in understanding how the model learns to weigh responses from different agents based on observations, leading to more accurate and reliable answers .
  4. Advantages Over Previous Methods:

    • The Multi-LLM framework offers improved accuracy in EQA tasks by leveraging multiple LLM-based agents and a central classifier for answer prediction, surpassing traditional aggregation methods .
    • The system's ability to work without communication during inference reduces time costs and enhances efficiency in answering embodied questions .

In conclusion, the Multi-LLM framework presented in the paper demonstrates superior performance, practicality, and efficiency in Embodied Question Answering tasks compared to previous methods, showcasing advancements in the field of AI research .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies exist in the field of embodied question answering via Multi-LLM systems. Noteworthy researchers in this area include Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Shaokun Zhang, Erkang Zhu, Beibin Li, Li Jiang, Xiaoyun Zhang, Chi Wang, Mikael Henaff, Sneha Silwal, Paul Mcvay, Oleksandr Maksymets, Sergio Arnaud, Karmesh Yadav, Qiyang Li, Ben Newman, Mohit Sharma, Vincent Berges, Shiqi Zhang, Pulkit Agrawal, Yonatan Bisk, Dhruv Batra, Mrinal Kalakrishnan, Franziska Meier, Chris Paxton, Sasha Sax, Aravind Rajeswaran, and many others .

The key to the solution mentioned in the paper involves adapting the Multi-LLM setup for Embodied Question Answering (EQA) in dynamic household environments, exploring aggregation methods for situational, subjective, and non-binary questions, and applying the CAM aggregation method to question-answering tasks beyond Embodied AI, such as long video understanding. Additionally, the quality of observation data, relying on the GLIP model, plays a crucial role in the results obtained .


How were the experiments in the paper designed?

The experiments in the paper were designed with a structured approach:

  • The experiments involved evaluating a Multi-LLM framework by gathering observations using a state-of-the-art exploration method .
  • The experiments followed a setup where observations were collected and stored by specific agents during an exploration phase, and the answers from the LLM were stored offline .
  • The experimentation process included using a 95%-5% random train-test split over 5 seeds and performing experiments in two different environments with varying numbers of nodes .
  • The experiments aimed to measure the accuracy of CAM for Embodied Question Answering (EQA) and demonstrate its performance over other aggregation baselines .
  • The experiments involved comparing the performance of various CAM methods and baselines in different graph environments, showcasing the superiority of CAM methods in terms of accuracy .
  • The experiments also included a feature importance analysis to understand how input impacts the model's final answer in the Multi-LLM system .
  • The experiments were structured to evaluate the inference time accuracy of the model and compare the performance of CAM against baseline aggregation approaches using various machine learning algorithms .
  • The experiments were designed to highlight the benefits of the Multi-LLM system, the practicality of using CAM for EQA, and the efficiency of the system in real-world scenarios .

What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is not explicitly mentioned in the provided contexts. However, the study focuses on improving factual-ity and reasoning in language models through multi-agent debate . Regarding the open-source code, the information about the code being open source is not provided in the contexts. It is recommended to refer directly to the study or contact the authors for specific details on the dataset and the availability of the code .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that needed verification. The study conducted a comprehensive analysis of a Multi-LLM framework for Embodied Question Answering (EQA) in dynamic household environments . The experiments involved evaluating the performance of the CAM model against baselines in different Matterport environments, demonstrating the superiority of the CAM model in terms of accuracy during test-time inference . This analysis showcases the effectiveness of the Multi-LLM system in real-world scenarios, emphasizing its practicality and performance .

Furthermore, the study delved into feature importance analysis within the Multi-LLM system, which is a crucial metric to understand how input impacts the model's final answer . By providing insights into the feature importance of the system, the research contributes to a deeper understanding of the functioning and decision-making processes of the Multi-LLM framework in EQA tasks .

Overall, the experiments conducted in the paper, along with the results obtained, offer strong empirical evidence to support the scientific hypotheses under investigation. The detailed analysis of the Multi-LLM framework's performance, feature importance, and practical application in dynamic environments contributes significantly to the advancement of research in Embodied Question Answering .


What are the contributions of this paper?

The contributions of this paper include:

  • Tackling Embodied EQA with LLM-based Multi-agent systems .
  • Providing a feature importance analysis on the framework .
  • Introducing a central classifier trained on independent answers from multiple agents to eliminate the need for communication .

What work can be continued in depth?

To further advance the research in this area, several avenues can be explored based on the existing work:

  • Adapting the Multi-LLM setup for Embodied Question Answering (EQA) in dynamic household environments where ground truth labels are challenging to obtain due to constantly changing non-stationary items .
  • Exploring aggregation methods for situational, subjective, and non-binary questions to enhance practicality in EQA tasks beyond binary "Yes/No" queries .
  • Analyzing the impact of different scene understanding algorithms on the quality of observation data in the Multi-LLM framework for EQA .
  • Extending the application of the CAM aggregation method to question-answering tasks outside of Embodied AI, such as long video understanding .
  • Investigating the potential of Multi-LLM systems for simultaneous exploration of household environments to improve efficiency in EQA tasks .
  • Addressing the challenge of conflicting responses from LLM-based agents in a multi-agent setup by exploring methods to reconcile varying responses .
  • Considering the removal of the need for communication by training a central classifier on independent answers from multiple agents to streamline the EQA process .
Tables
1
Scan the QR code to ask more questions about the paper
© 2025 Powerdrill. All rights reserved.