Embodied Question Answering via Multi-LLM Systems

Bhrij Patel, Vishnu Sashank Dorbala, Dinesh Manocha, 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, making it more efficient. The study tests various aggregation algorithms and analyzes feature importance to understand CAM's performance. It leverages multi-agent systems to improve exploration and answer accuracy, particularly in cohabited spaces, and demonstrates the effectiveness of the approach using the Matterport3D dataset. Future work will focus on expanding the framework to handle more complex questions and dynamic environments, as well as exploring the potential of LLMs in other AI tasks.

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 training a Central Answer Model (CAM) to aggregate responses from multiple agents and predict answers to binary embodied questions about a household environment . This problem is not entirely new, as prior work has explored ensemble LLM methods and consensus-reaching debates between LLM agents to tackle similar challenges . However, the paper introduces a unique approach by training a central classifier on independent agent answers without the need for communication between agents, thereby enhancing the efficiency and accuracy of the EQA system .


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate the scientific hypothesis related to Embodied Question Answering (EQA) using a novel Multi-LLM agent approach . The hypothesis revolves around training a Central Answer Model (CAM) on the answers provided by independent agents to predict answers to binary embodied questions about a household environment . The main goal is to address the challenges in EQA by aggregating multiple agent responses to enhance accuracy and decision-making without the need for communication between agents . The paper aims to demonstrate the effectiveness of this approach in improving factual accuracy and reasoning in language models for EQA tasks .


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 agent 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 agents to predict an answer .
    • CAM is trained on labeled query datasets using various machine learning methods, demonstrating up to 50% higher accuracy compared to traditional majority vote and debate aggregation methods .
  2. Integration with Exploration Systems:

    • The proposed framework is evaluated on data collected using an LLM-based exploration method on multiple agents in Matterport3D environments. This shows that the system can work in conjunction with state-of-the-art exploration methods in unknown settings .
  3. Reduced Communication Costs:

    • The Multi-LLM agent approach eliminates the need for communication between agents by directly outputting the final answer based on observations and training a Central Answer Model .
    • The model learns to identify reliable agents to rely on, reducing the end user's effort in determining the best agent response .
  4. Addressing Vulnerabilities:

    • The paper addresses vulnerabilities in ensemble LLM methods by avoiding scenarios where incorrect answers from poor-performing agents influence the overall decision. The proposed model aims to learn to identify incorrect agents and prevent their influence on the final answer .

Overall, the paper's contributions include a novel Multi-LLM EQA framework with the CAM, integration with exploration systems, and a focus on reducing communication costs and addressing vulnerabilities in traditional ensemble LLM methods . The paper introduces a novel Multi-LLM EQA framework with a Central Answer Model (CAM) that offers several characteristics and advantages compared to previous methods . Here are the key points highlighted in the paper:

  1. Central Answer Model (CAM):

    • CAM acts as a classifier that aggregates responses from multiple agents to predict an answer in an Embodied Question Answering (EQA) setting .
    • The CAM methods outperform traditional baselines like majority vote (MV) and debate baselines, with XGBoost achieving significantly higher accuracy, up to 50% higher than MV and 33% higher than debating baselines .
    • CAM reduces the end user's effort by learning which agents to rely on, enhancing the overall accuracy of the system .
  2. Integration with Exploration Systems:

    • The Multi-LLM system can work in conjunction with state-of-the-art exploration methods in unknown settings, demonstrating practicality in real-world use cases .
    • By utilizing Language-Guided Exploration (LGX) for observation gathering, the CAM model consistently outperforms non-learning aggregation baselines, showcasing the effectiveness of the proposed framework .
  3. Reduced Communication Costs:

    • The Multi-LLM approach eliminates the need for communication between agents, as the Central Answer Model directly outputs the final answer based on observations, reducing inference time significantly compared to debate-based approaches .
  4. Addressing Vulnerabilities:

    • The paper addresses vulnerabilities in ensemble LLM methods by focusing on learning to identify incorrect agents and prevent their influence on the final answer, ensuring the reliability of the system .
    • The CAM model's feature importance analysis highlights the reliance on each independent agent, providing insights into the decision-making process and enhancing the model's robustness .

Overall, the proposed Multi-LLM EQA framework with CAM offers improved accuracy, reduced communication costs, integration with exploration systems, and enhanced reliability compared to traditional methods, making it a promising approach for Embodied Question Answering tasks .


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?

In the field of embodied question answering via Multi-LLM systems, there are several related research works and notable researchers:

  • Noteworthy researchers in this field include Sinan Tan, Weilai Xiang, Huaping Liu, Di Guo, and Fuchun Sun .
  • Other prominent researchers are 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, and Aravind Rajeswaran .
  • The key to the solution mentioned in the paper involves utilizing Multi-LLM systems for embodied question answering in interactive environments, focusing on factors like observation data quality and scene understanding algorithms .

How were the experiments in the paper designed?

The experiments in the paper were designed with a specific setup:

  • The experiments involved using a Multi-LLM system with observations collected from different rooms in a household environment .
  • The experiments were conducted in two different environments: one with 215 nodes and 15 distinct rooms, and the other with 53 nodes and 12 distinct rooms .
  • The experiments included a 95%-5% random train-test split over 5 seeds for the setup .
  • The performance of the CAM methods and baselines was evaluated in these environments, with CAM methods consistently outperforming the baselines .
  • The experiments highlighted the importance of model selection and tuning, as well as the impact of ground truth labels on model performance .
  • The experiments also focused on the practicality of the Multi-LLM system in real-world scenarios, emphasizing the effectiveness of the system in conjunction with an LLM-based exploration method .
  • The experiments involved training a model to give final "Yes/No" outputs based on inputs from multiple agents in the Multi-LLM system .
  • Various machine learning algorithms were used to train the CAM models, including Neural Network, Random Forest, Decision Tree, XGBoost, and SVM, among others .
  • The experiments compared the CAM approach against baseline aggregation methods like Majority Vote (MV) and Debate, showcasing the superiority of CAM methods in terms of accuracy and performance .

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

The dataset used for quantitative evaluation in the study is the Matterport environments dataset, which includes environments with 215 nodes and 53 nodes for experimentation . The code for the study is not explicitly mentioned to be open source in the provided context.


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 outlines several key findings that contribute to the validation of the hypotheses . The experiments conducted demonstrate the effectiveness of the Multi-LLM system in Embodied Question Answering (EQA) tasks, particularly in dynamic household environments . The results indicate that the CAM model outperforms baselines in terms of test-time accuracy, showcasing the practicality and efficiency of the Multi-LLM system . Additionally, the feature importance analysis conducted in the study sheds light on how input impacts the model's final answer, further reinforcing the scientific hypotheses . The research also highlights the importance of scene understanding algorithms and the quality of observation data in influencing the outcomes of the experiments .


What are the contributions of this paper?

The contributions of the paper include:

  • Introducing a Multi-LLM setup for Embodied Question Answering (EQA) tasks .
  • Addressing limitations such as the challenges of obtaining ground truth labels in dynamic household environments and focusing on binary "Yes/No" questions, suggesting future research directions for more practical and diverse question types .
  • Proposing the use of a CAM aggregation method for question-answering tasks beyond Embodied AI, such as long video understanding .
  • Analyzing the impact of scene understanding algorithms on the quality of observation data in the Multi-LLM setup .

What work can be continued in depth?

To further advance the research in Embodied Question Answering (EQA) using Multi-LLM systems, several areas can be explored in depth based on the limitations and future work outlined in the provided context :

  • Adapting the Multi-LLM setup for 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 beyond binary "Yes/No" queries .
  • Extending the application of the CAM aggregation method to question-answering tasks outside of Embodied AI, such as long video understanding .
  • Analyzing the impact of different scene understanding algorithms on the quality of observation data in the EQA setup .

These areas present promising directions for future research to enhance the capabilities and effectiveness of Multi-LLM systems in the context of Embodied Question Answering.

Tables

1

Introduction
Background
Evolution of embodied AI and EQA
Importance of household environments in AI research
Objective
To develop and evaluate CAM for EQA
Improve accuracy and efficiency in multi-agent systems
Investigate LLMs' potential in household tasks
Method
Data Collection
Matterport3D dataset: Description and usage
Real-world and simulated household environments
Data Preprocessing
Cleaning and filtering of LLM responses
Standardization of input and output data
Central Answer Model (CAM)
CAM Architecture
Integration of individual LLMs
Aggregation algorithms: Comparison and selection
Performance Evaluation
Accuracy improvements over ensemble methods
Efficiency comparison with agent communication
Feature Importance Analysis
Identifying key factors in CAM's success
Exploring the role of LLMs in decision-making
Results and Discussion
CAM Performance in EQA
Accuracy boost in household environments
Case studies and examples
Exploration and Answer Accuracy
Multi-agent collaboration benefits
Challenges and limitations in cohabited spaces
Future Work
Expanding the Framework
Complex questions and dynamic environments
Integration with more advanced LLMs
Applications and Extensions
LLMs in other AI tasks: Potential and challenges
Conclusion
Summary of findings and contributions
Implications for the field of embodied AI and LLMs
Directions for future research in multi-agent EQA systems.
Basic info
papers
computation and language
machine learning
artificial intelligence
Advanced features
Insights
What is the significance of leveraging multi-agent systems in the study's approach?
What improvement in accuracy does CAM achieve compared to ensemble methods like voting?
How does the Central Answer Model (CAM) differ from ensemble methods in the context of Embodied Question Answering?
What is the primary focus of the paper?

Embodied Question Answering via Multi-LLM Systems

Bhrij Patel, Vishnu Sashank Dorbala, Dinesh Manocha, 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, making it more efficient. The study tests various aggregation algorithms and analyzes feature importance to understand CAM's performance. It leverages multi-agent systems to improve exploration and answer accuracy, particularly in cohabited spaces, and demonstrates the effectiveness of the approach using the Matterport3D dataset. Future work will focus on expanding the framework to handle more complex questions and dynamic environments, as well as exploring the potential of LLMs in other AI tasks.
Mind map
Exploring the role of LLMs in decision-making
Identifying key factors in CAM's success
LLMs in other AI tasks: Potential and challenges
Integration with more advanced LLMs
Complex questions and dynamic environments
Challenges and limitations in cohabited spaces
Multi-agent collaboration benefits
Case studies and examples
Accuracy boost in household environments
Feature Importance Analysis
Aggregation algorithms: Comparison and selection
Integration of individual LLMs
Central Answer Model (CAM)
Real-world and simulated household environments
Matterport3D dataset: Description and usage
Investigate LLMs' potential in household tasks
Improve accuracy and efficiency in multi-agent systems
To develop and evaluate CAM for EQA
Importance of household environments in AI research
Evolution of embodied AI and EQA
Directions for future research in multi-agent EQA systems.
Implications for the field of embodied AI and LLMs
Summary of findings and contributions
Applications and Extensions
Expanding the Framework
Exploration and Answer Accuracy
CAM Performance in EQA
Performance Evaluation
CAM Architecture
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Future Work
Results and Discussion
Method
Introduction
Outline
Introduction
Background
Evolution of embodied AI and EQA
Importance of household environments in AI research
Objective
To develop and evaluate CAM for EQA
Improve accuracy and efficiency in multi-agent systems
Investigate LLMs' potential in household tasks
Method
Data Collection
Matterport3D dataset: Description and usage
Real-world and simulated household environments
Data Preprocessing
Cleaning and filtering of LLM responses
Standardization of input and output data
Central Answer Model (CAM)
CAM Architecture
Integration of individual LLMs
Aggregation algorithms: Comparison and selection
Performance Evaluation
Accuracy improvements over ensemble methods
Efficiency comparison with agent communication
Feature Importance Analysis
Identifying key factors in CAM's success
Exploring the role of LLMs in decision-making
Results and Discussion
CAM Performance in EQA
Accuracy boost in household environments
Case studies and examples
Exploration and Answer Accuracy
Multi-agent collaboration benefits
Challenges and limitations in cohabited spaces
Future Work
Expanding the Framework
Complex questions and dynamic environments
Integration with more advanced LLMs
Applications and Extensions
LLMs in other AI tasks: Potential and challenges
Conclusion
Summary of findings and contributions
Implications for the field of embodied AI and LLMs
Directions for future research in multi-agent EQA systems.
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 training a Central Answer Model (CAM) to aggregate responses from multiple agents and predict answers to binary embodied questions about a household environment . This problem is not entirely new, as prior work has explored ensemble LLM methods and consensus-reaching debates between LLM agents to tackle similar challenges . However, the paper introduces a unique approach by training a central classifier on independent agent answers without the need for communication between agents, thereby enhancing the efficiency and accuracy of the EQA system .


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate the scientific hypothesis related to Embodied Question Answering (EQA) using a novel Multi-LLM agent approach . The hypothesis revolves around training a Central Answer Model (CAM) on the answers provided by independent agents to predict answers to binary embodied questions about a household environment . The main goal is to address the challenges in EQA by aggregating multiple agent responses to enhance accuracy and decision-making without the need for communication between agents . The paper aims to demonstrate the effectiveness of this approach in improving factual accuracy and reasoning in language models for EQA tasks .


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 agent 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 agents to predict an answer .
    • CAM is trained on labeled query datasets using various machine learning methods, demonstrating up to 50% higher accuracy compared to traditional majority vote and debate aggregation methods .
  2. Integration with Exploration Systems:

    • The proposed framework is evaluated on data collected using an LLM-based exploration method on multiple agents in Matterport3D environments. This shows that the system can work in conjunction with state-of-the-art exploration methods in unknown settings .
  3. Reduced Communication Costs:

    • The Multi-LLM agent approach eliminates the need for communication between agents by directly outputting the final answer based on observations and training a Central Answer Model .
    • The model learns to identify reliable agents to rely on, reducing the end user's effort in determining the best agent response .
  4. Addressing Vulnerabilities:

    • The paper addresses vulnerabilities in ensemble LLM methods by avoiding scenarios where incorrect answers from poor-performing agents influence the overall decision. The proposed model aims to learn to identify incorrect agents and prevent their influence on the final answer .

Overall, the paper's contributions include a novel Multi-LLM EQA framework with the CAM, integration with exploration systems, and a focus on reducing communication costs and addressing vulnerabilities in traditional ensemble LLM methods . The paper introduces a novel Multi-LLM EQA framework with a Central Answer Model (CAM) that offers several characteristics and advantages compared to previous methods . Here are the key points highlighted in the paper:

  1. Central Answer Model (CAM):

    • CAM acts as a classifier that aggregates responses from multiple agents to predict an answer in an Embodied Question Answering (EQA) setting .
    • The CAM methods outperform traditional baselines like majority vote (MV) and debate baselines, with XGBoost achieving significantly higher accuracy, up to 50% higher than MV and 33% higher than debating baselines .
    • CAM reduces the end user's effort by learning which agents to rely on, enhancing the overall accuracy of the system .
  2. Integration with Exploration Systems:

    • The Multi-LLM system can work in conjunction with state-of-the-art exploration methods in unknown settings, demonstrating practicality in real-world use cases .
    • By utilizing Language-Guided Exploration (LGX) for observation gathering, the CAM model consistently outperforms non-learning aggregation baselines, showcasing the effectiveness of the proposed framework .
  3. Reduced Communication Costs:

    • The Multi-LLM approach eliminates the need for communication between agents, as the Central Answer Model directly outputs the final answer based on observations, reducing inference time significantly compared to debate-based approaches .
  4. Addressing Vulnerabilities:

    • The paper addresses vulnerabilities in ensemble LLM methods by focusing on learning to identify incorrect agents and prevent their influence on the final answer, ensuring the reliability of the system .
    • The CAM model's feature importance analysis highlights the reliance on each independent agent, providing insights into the decision-making process and enhancing the model's robustness .

Overall, the proposed Multi-LLM EQA framework with CAM offers improved accuracy, reduced communication costs, integration with exploration systems, and enhanced reliability compared to traditional methods, making it a promising approach for Embodied Question Answering tasks .


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?

In the field of embodied question answering via Multi-LLM systems, there are several related research works and notable researchers:

  • Noteworthy researchers in this field include Sinan Tan, Weilai Xiang, Huaping Liu, Di Guo, and Fuchun Sun .
  • Other prominent researchers are 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, and Aravind Rajeswaran .
  • The key to the solution mentioned in the paper involves utilizing Multi-LLM systems for embodied question answering in interactive environments, focusing on factors like observation data quality and scene understanding algorithms .

How were the experiments in the paper designed?

The experiments in the paper were designed with a specific setup:

  • The experiments involved using a Multi-LLM system with observations collected from different rooms in a household environment .
  • The experiments were conducted in two different environments: one with 215 nodes and 15 distinct rooms, and the other with 53 nodes and 12 distinct rooms .
  • The experiments included a 95%-5% random train-test split over 5 seeds for the setup .
  • The performance of the CAM methods and baselines was evaluated in these environments, with CAM methods consistently outperforming the baselines .
  • The experiments highlighted the importance of model selection and tuning, as well as the impact of ground truth labels on model performance .
  • The experiments also focused on the practicality of the Multi-LLM system in real-world scenarios, emphasizing the effectiveness of the system in conjunction with an LLM-based exploration method .
  • The experiments involved training a model to give final "Yes/No" outputs based on inputs from multiple agents in the Multi-LLM system .
  • Various machine learning algorithms were used to train the CAM models, including Neural Network, Random Forest, Decision Tree, XGBoost, and SVM, among others .
  • The experiments compared the CAM approach against baseline aggregation methods like Majority Vote (MV) and Debate, showcasing the superiority of CAM methods in terms of accuracy and performance .

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

The dataset used for quantitative evaluation in the study is the Matterport environments dataset, which includes environments with 215 nodes and 53 nodes for experimentation . The code for the study is not explicitly mentioned to be open source in the provided context.


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 outlines several key findings that contribute to the validation of the hypotheses . The experiments conducted demonstrate the effectiveness of the Multi-LLM system in Embodied Question Answering (EQA) tasks, particularly in dynamic household environments . The results indicate that the CAM model outperforms baselines in terms of test-time accuracy, showcasing the practicality and efficiency of the Multi-LLM system . Additionally, the feature importance analysis conducted in the study sheds light on how input impacts the model's final answer, further reinforcing the scientific hypotheses . The research also highlights the importance of scene understanding algorithms and the quality of observation data in influencing the outcomes of the experiments .


What are the contributions of this paper?

The contributions of the paper include:

  • Introducing a Multi-LLM setup for Embodied Question Answering (EQA) tasks .
  • Addressing limitations such as the challenges of obtaining ground truth labels in dynamic household environments and focusing on binary "Yes/No" questions, suggesting future research directions for more practical and diverse question types .
  • Proposing the use of a CAM aggregation method for question-answering tasks beyond Embodied AI, such as long video understanding .
  • Analyzing the impact of scene understanding algorithms on the quality of observation data in the Multi-LLM setup .

What work can be continued in depth?

To further advance the research in Embodied Question Answering (EQA) using Multi-LLM systems, several areas can be explored in depth based on the limitations and future work outlined in the provided context :

  • Adapting the Multi-LLM setup for 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 beyond binary "Yes/No" queries .
  • Extending the application of the CAM aggregation method to question-answering tasks outside of Embodied AI, such as long video understanding .
  • Analyzing the impact of different scene understanding algorithms on the quality of observation data in the EQA setup .

These areas present promising directions for future research to enhance the capabilities and effectiveness of Multi-LLM systems in the context of Embodied Question Answering.

Tables
1
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