POEM: Interactive Prompt Optimization for Enhancing Multimodal Reasoning of Large Language Models

Jianben He, Xingbo Wang, Shiyi Liu, Guande Wu, Claudio Silva, Huamin Qu·June 06, 2024

Summary

This paper introduces POEM, a visual analytics system that enhances large language models' (LLMs) multimodal reasoning by streamlining prompt engineering. POEM addresses the lack of systems that consider the complex interplay between different modalities and enables users to analyze model performance, refine prompts, and align model knowledge with human insights. Key features include a sampling strategy, LLM-assisted summarization, and a framework for expert model processing, multimodal understanding, prompt iteration, and a user interface. Case studies and expert interviews demonstrate the system's effectiveness and efficiency, emphasizing the importance of multimodal reasoning and prompt engineering in improving LLM performance for tasks like sentiment analysis and user intent understanding. The research highlights the need for human-guided model steering and the potential of principles for human-AI knowledge alignment, while also acknowledging limitations in scalability and handling large datasets.

Key findings

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Paper digest

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

The paper aims to address deficiencies in current Large Language Models (LLMs) related to producing hallucinated and inconsistent responses by focusing on prompt engineering for enhancing multimodal reasoning . This problem is not entirely new, as previous studies have explored issues such as model interpretability, post-hoc interpretability for neural NLP, and characterizing large language models for knowledge-intensive tasks . The paper introduces a novel visual analytics tool, POEM, to facilitate prompt engineering with human insight and expertise, allowing users to assess prompt effectiveness and apply varied strategies for prompt revision . The system's efficacy and efficiency have been validated through case studies and positive feedback from experts .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis that prompt engineering can enhance multimodal reasoning of Large Language Models (LLMs) by leveraging human insight and expertise . The study introduces a visual analytics tool called POEM, designed to facilitate prompt engineering for LLMs, enabling users to assess prompt effectiveness, revise prompts, and apply their knowledge for efficient prompt iteration . The system's efficacy and efficiency were validated through case studies and positive feedback from experts .


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

The paper introduces a novel visual analytics tool called POEM, designed to enhance multimodal reasoning of Large Language Models (LLMs) through prompt engineering with human insight and expertise . The system enables users to evaluate prompt effectiveness, offers strategies for prompt revision, and validates its efficacy through case studies and positive feedback from experts .

One key aspect of the paper is the implementation of batch processing to expedite data processing and generation for a smoother prompting experience. However, this approach may not be sufficient for handling large-scale data, prompting the exploration of strategies like parallel computing and data sampling to ensure instant feedback . Additionally, the paper discusses the visual design challenges, such as the potential visual clutter in the Sankey diagram design when dealing with a large number of prediction classes or complex modalities, suggesting the adoption of hierarchical visualization design for enhanced visual scalability .

The paper also addresses the limitations of current LLMs in producing hallucinated and inconsistent responses. To mitigate this issue, the system fixes hyperparameters, provides a multi-level systematic analysis of outputs for different prompts, and allows users to easily identify outlying responses. Future work is proposed to focus on developing techniques to reduce hallucination occurrence in model outputs and integrating more advanced expert models to visualize potential uncertainties for increased user trust .

Furthermore, the paper discusses the system's framework, which comprises four primary modules: processing visual and language modality information, understanding multimodal reasoning, recommending prompt iteration strategies, and facilitating prompt performance examination, refinement, and monitoring. The system aims to refine prompts by providing more precise task descriptions, clear principles for the model to follow, and illustrative examples to aid the model in understanding relationships .

In terms of future work, the paper suggests exploring techniques to reduce hallucination occurrence in model outputs, integrating advanced expert models for increased user trust, enabling comparison across multiple LLMs, and extending the study to involve more modalities in complex scenarios and applications . The system's scalability is rooted in its algorithm and visual design, with potential extensions to investigate interactions between multiple modalities and tasks beyond the visual and language modalities . The POEM system introduces several key characteristics and advantages compared to previous methods for enhancing multimodal reasoning of Large Language Models (LLMs) .

  1. Efficient Prompt Iteration: POEM offers a systematic and comprehensive approach to prompt engineering, enhancing prompt iteration efficiency compared to traditional methods that rely solely on performance statistics. Experts noted that POEM provides varied strategies and a streamlined process that reduces the complexity of prompt writing and testing, making it easier to analyze model multimodal reasoning performance .

  2. Intuitive Design and Interactions: Experts praised the intuitive visual and interaction design of POEM, highlighting features like the Prompt History design that facilitates tracking changes and the generate and import at one-click function that saves time in prompt editing. The system's capability to examine and evaluate instance-level details enhances user trust in the prompts developed .

  3. Detailed Multimodal Reasoning Patterns: POEM allows users to explore detailed multimodal reasoning patterns through a structured interface. The system provides a visual representation of modality interactions, enabling users to understand how visual and language cues are combined for reasoning. Users can analyze patterns, evidence, and instances to gain insights into model performance .

  4. Model-Agnostic and Scalable: The approach adopted by POEM is designed to be model-agnostic, allowing easy adaptation to various multimodal content comprehension tasks and LLMs. The system's scalability is rooted in its algorithm and visual design, enabling extensions to investigate interactions between multiple modalities and tasks beyond visual and language modalities. This scalability makes POEM a versatile tool for different applications beyond prompt optimization .

  5. Expert Validation and Usability: Through expert interviews, POEM was validated for its effectiveness and usability by academic and industry researchers experienced in prompt engineering and multimodal LLM training. Experts appreciated the system's workflow, design, and interaction features, highlighting its efficiency in prompt iteration and instance-level evaluation, which enhances user confidence in prompt development .

In summary, POEM stands out for its user-friendly design, efficient prompt iteration process, detailed multimodal reasoning pattern exploration, model-agnostic approach, and scalability for various multimodal tasks, making it a valuable tool for enhancing multimodal reasoning of Large Language Models .


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 enhancing multimodal reasoning of Large Language Models (LLMs). Noteworthy researchers in this field include P. P. Liang, Y. Cheng, R. Salakhutdinov, L.-P. Morency, S. Gehrmann, M. N. Hoque, W. He, A. K. Shekar, S. Huang, L. Dong, W. Wang, Y. Hao, S. Singhal, among others . These researchers have contributed to various aspects of multimodal interactions, fusion, visualization, and understanding of multimodal models.

The key to the solution mentioned in the paper "POEM: Interactive Prompt Optimization for Enhancing Multimodal Reasoning of Large Language Models" is the development of a visual analytics tool called POEM. This tool is designed to facilitate prompt engineering for improving the multimodal reasoning of LLMs by incorporating human insight and expertise. It allows users to assess prompt effectiveness, revise prompts efficiently, and apply their knowledge to enhance prompt iteration for targeted downstream tasks . The tool aims to empower model practitioners to align and enhance model performance with their expertise through prompting, thereby improving the overall reasoning capabilities of Large Language Models.


How were the experiments in the paper designed?

The experiments in the paper were designed through a systematic approach involving two case studies and expert interviews to validate the efficacy and efficiency of the POEM system . The experiments included introducing POEM, demonstrating its workflow and functions, allowing experts to explore the system using real datasets, and gathering feedback through semi-structured interviews with academic researchers and industry research scientists . The experts verified the effectiveness and usability of POEM, highlighting the thoughtful design of the workflow, the intuitive visual and interaction design, and the system's capability to examine and evaluate at the instance level with reference to raw data . The experiments aimed to assess prompt effectiveness, offer strategies for prompt revision, and enable users to apply their knowledge for efficient prompt iteration .


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

The dataset used for quantitative evaluation in the study is the CMU-MOSEI dataset for multimodal sentiment analysis . 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 strong support for the scientific hypotheses that needed verification. The study conducted tests to evaluate prompt robustness, achieving an accuracy of 75% with correctly predicted test instances . Additionally, the paper introduces POEM, a visual analytics tool designed to enhance prompt engineering for improving multimodal reasoning of Large Language Models (LLMs) with human insight and expertise. The system allows users to assess prompt effectiveness, revise prompts, and apply their knowledge for efficient prompt iteration, as validated through case studies and positive feedback from experts .

Furthermore, the research explores various aspects related to multimodal interactions, quantifying and modeling these interactions, as well as studying human and automatic quantification of multimodal fusion interactions . The experiments delve into semi-supervised multimodal learning, visualizing and understanding multimodal models, and quantifying interactions in these models . These analyses contribute to a comprehensive understanding of how multimodal reasoning operates within LLMs, supporting the scientific hypotheses and enhancing the knowledge base in this domain.


What are the contributions of this paper?

The paper introduces POEM, a visual analytics tool designed to enhance multimodal reasoning of Large Language Models (LLMs) through prompt engineering with human insight and expertise. The contributions of the paper include:

  • Introducing a novel visual analytics tool, POEM, that facilitates prompt engineering for enhancing multimodal reasoning of LLMs .
  • Allowing users to assess prompt effectiveness, summarize multimodal reasoning patterns, and provide strategies for prompt revision, enabling efficient prompt iteration .
  • Validating the system's efficacy and efficiency through two case studies and receiving positive feedback from experts .

What work can be continued in depth?

Further research can delve deeper into several aspects related to enhancing multimodal reasoning performance of Large Language Models (LLMs) based on the information provided in the context:

  • Reducing Hallucination: Future efforts can focus on developing techniques to reduce hallucination occurrences in model outputs, which is a current deficiency in Large Language Models .
  • Integration of Advanced Expert Models: There is a potential to integrate more advanced expert models to mitigate potential information loss or inaccuracies and visualize uncertainties to increase user trust .
  • Comparison Across Multiple LLMs: Extending the work to enable comparison across multiple LLMs can provide insights into effective prompt engineering strategies for different models and tasks .
  • Interaction Involving More Modalities: The research can be extended to study interaction involving more modalities in increasingly complex scenarios and applications to enhance multimodal reasoning performance .

Introduction
Background
Evolution of large language models (LLMs) and multimodal reasoning
Current limitations in prompt engineering and multimodal analysis
Objective
To develop and evaluate POEM: a system for improving LLM performance
Addressing the need for human-guided model steering and multimodal understanding
Method
Data Collection
Selection of LLMs and datasets for evaluation
Gathering existing prompts and model performance data
Data Preprocessing
Cleaning and standardizing multimodal data
Integration of LLM outputs and human annotations
Key Features
Sampling Strategy
Designing a method for efficient prompt selection and generation
LLM-Assisted Summarization
Utilizing LLMs to summarize and refine prompts
Expert Model Processing
Framework for experts to analyze and process model outputs
Multimodal Understanding
Analyzing the interplay between different modalities
Prompt Iteration
Iterative process for refining prompts based on user feedback
User Interface
Development of a user-friendly interface for interaction
Case Studies
Sentiment Analysis
Application of POEM and impact on model performance
User Intent Understanding
Real-world scenarios showcasing the system's effectiveness
Expert Interviews
Gathering insights on system usability and effectiveness
Identifying best practices and areas for improvement
Evaluation
Quantitative analysis of system performance and efficiency
Qualitative assessment through user feedback and expert opinions
Limitations and Scalability
Addressing challenges in handling large datasets and scalability
Future directions for overcoming these limitations
Conclusion
Summary of key findings and contributions
Implications for human-AI knowledge alignment and model steering
Recommendations for future research in visual analytics for LLMs
Basic info
papers
human-computer interaction
artificial intelligence
Advanced features
Insights
How does POEM address the issue of prompt engineering for large language models?
What is the primary focus of the POEM system introduced in the paper?
What are the key features of the POEM system that facilitate multimodal reasoning and user interaction?
What are the case studies and expert interviews in the paper used to demonstrate?

POEM: Interactive Prompt Optimization for Enhancing Multimodal Reasoning of Large Language Models

Jianben He, Xingbo Wang, Shiyi Liu, Guande Wu, Claudio Silva, Huamin Qu·June 06, 2024

Summary

This paper introduces POEM, a visual analytics system that enhances large language models' (LLMs) multimodal reasoning by streamlining prompt engineering. POEM addresses the lack of systems that consider the complex interplay between different modalities and enables users to analyze model performance, refine prompts, and align model knowledge with human insights. Key features include a sampling strategy, LLM-assisted summarization, and a framework for expert model processing, multimodal understanding, prompt iteration, and a user interface. Case studies and expert interviews demonstrate the system's effectiveness and efficiency, emphasizing the importance of multimodal reasoning and prompt engineering in improving LLM performance for tasks like sentiment analysis and user intent understanding. The research highlights the need for human-guided model steering and the potential of principles for human-AI knowledge alignment, while also acknowledging limitations in scalability and handling large datasets.
Mind map
Development of a user-friendly interface for interaction
User Interface
Iterative process for refining prompts based on user feedback
Prompt Iteration
Analyzing the interplay between different modalities
Multimodal Understanding
Framework for experts to analyze and process model outputs
Expert Model Processing
Utilizing LLMs to summarize and refine prompts
LLM-Assisted Summarization
Designing a method for efficient prompt selection and generation
Sampling Strategy
Future directions for overcoming these limitations
Addressing challenges in handling large datasets and scalability
Identifying best practices and areas for improvement
Gathering insights on system usability and effectiveness
Key Features
Gathering existing prompts and model performance data
Selection of LLMs and datasets for evaluation
Addressing the need for human-guided model steering and multimodal understanding
To develop and evaluate POEM: a system for improving LLM performance
Current limitations in prompt engineering and multimodal analysis
Evolution of large language models (LLMs) and multimodal reasoning
Recommendations for future research in visual analytics for LLMs
Implications for human-AI knowledge alignment and model steering
Summary of key findings and contributions
Limitations and Scalability
Expert Interviews
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Evaluation
Case Studies
Method
Introduction
Outline
Introduction
Background
Evolution of large language models (LLMs) and multimodal reasoning
Current limitations in prompt engineering and multimodal analysis
Objective
To develop and evaluate POEM: a system for improving LLM performance
Addressing the need for human-guided model steering and multimodal understanding
Method
Data Collection
Selection of LLMs and datasets for evaluation
Gathering existing prompts and model performance data
Data Preprocessing
Cleaning and standardizing multimodal data
Integration of LLM outputs and human annotations
Key Features
Sampling Strategy
Designing a method for efficient prompt selection and generation
LLM-Assisted Summarization
Utilizing LLMs to summarize and refine prompts
Expert Model Processing
Framework for experts to analyze and process model outputs
Multimodal Understanding
Analyzing the interplay between different modalities
Prompt Iteration
Iterative process for refining prompts based on user feedback
User Interface
Development of a user-friendly interface for interaction
Case Studies
Sentiment Analysis
Application of POEM and impact on model performance
User Intent Understanding
Real-world scenarios showcasing the system's effectiveness
Expert Interviews
Gathering insights on system usability and effectiveness
Identifying best practices and areas for improvement
Evaluation
Quantitative analysis of system performance and efficiency
Qualitative assessment through user feedback and expert opinions
Limitations and Scalability
Addressing challenges in handling large datasets and scalability
Future directions for overcoming these limitations
Conclusion
Summary of key findings and contributions
Implications for human-AI knowledge alignment and model steering
Recommendations for future research in visual analytics for LLMs
Key findings
2

Paper digest

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

The paper aims to address deficiencies in current Large Language Models (LLMs) related to producing hallucinated and inconsistent responses by focusing on prompt engineering for enhancing multimodal reasoning . This problem is not entirely new, as previous studies have explored issues such as model interpretability, post-hoc interpretability for neural NLP, and characterizing large language models for knowledge-intensive tasks . The paper introduces a novel visual analytics tool, POEM, to facilitate prompt engineering with human insight and expertise, allowing users to assess prompt effectiveness and apply varied strategies for prompt revision . The system's efficacy and efficiency have been validated through case studies and positive feedback from experts .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis that prompt engineering can enhance multimodal reasoning of Large Language Models (LLMs) by leveraging human insight and expertise . The study introduces a visual analytics tool called POEM, designed to facilitate prompt engineering for LLMs, enabling users to assess prompt effectiveness, revise prompts, and apply their knowledge for efficient prompt iteration . The system's efficacy and efficiency were validated through case studies and positive feedback from experts .


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

The paper introduces a novel visual analytics tool called POEM, designed to enhance multimodal reasoning of Large Language Models (LLMs) through prompt engineering with human insight and expertise . The system enables users to evaluate prompt effectiveness, offers strategies for prompt revision, and validates its efficacy through case studies and positive feedback from experts .

One key aspect of the paper is the implementation of batch processing to expedite data processing and generation for a smoother prompting experience. However, this approach may not be sufficient for handling large-scale data, prompting the exploration of strategies like parallel computing and data sampling to ensure instant feedback . Additionally, the paper discusses the visual design challenges, such as the potential visual clutter in the Sankey diagram design when dealing with a large number of prediction classes or complex modalities, suggesting the adoption of hierarchical visualization design for enhanced visual scalability .

The paper also addresses the limitations of current LLMs in producing hallucinated and inconsistent responses. To mitigate this issue, the system fixes hyperparameters, provides a multi-level systematic analysis of outputs for different prompts, and allows users to easily identify outlying responses. Future work is proposed to focus on developing techniques to reduce hallucination occurrence in model outputs and integrating more advanced expert models to visualize potential uncertainties for increased user trust .

Furthermore, the paper discusses the system's framework, which comprises four primary modules: processing visual and language modality information, understanding multimodal reasoning, recommending prompt iteration strategies, and facilitating prompt performance examination, refinement, and monitoring. The system aims to refine prompts by providing more precise task descriptions, clear principles for the model to follow, and illustrative examples to aid the model in understanding relationships .

In terms of future work, the paper suggests exploring techniques to reduce hallucination occurrence in model outputs, integrating advanced expert models for increased user trust, enabling comparison across multiple LLMs, and extending the study to involve more modalities in complex scenarios and applications . The system's scalability is rooted in its algorithm and visual design, with potential extensions to investigate interactions between multiple modalities and tasks beyond the visual and language modalities . The POEM system introduces several key characteristics and advantages compared to previous methods for enhancing multimodal reasoning of Large Language Models (LLMs) .

  1. Efficient Prompt Iteration: POEM offers a systematic and comprehensive approach to prompt engineering, enhancing prompt iteration efficiency compared to traditional methods that rely solely on performance statistics. Experts noted that POEM provides varied strategies and a streamlined process that reduces the complexity of prompt writing and testing, making it easier to analyze model multimodal reasoning performance .

  2. Intuitive Design and Interactions: Experts praised the intuitive visual and interaction design of POEM, highlighting features like the Prompt History design that facilitates tracking changes and the generate and import at one-click function that saves time in prompt editing. The system's capability to examine and evaluate instance-level details enhances user trust in the prompts developed .

  3. Detailed Multimodal Reasoning Patterns: POEM allows users to explore detailed multimodal reasoning patterns through a structured interface. The system provides a visual representation of modality interactions, enabling users to understand how visual and language cues are combined for reasoning. Users can analyze patterns, evidence, and instances to gain insights into model performance .

  4. Model-Agnostic and Scalable: The approach adopted by POEM is designed to be model-agnostic, allowing easy adaptation to various multimodal content comprehension tasks and LLMs. The system's scalability is rooted in its algorithm and visual design, enabling extensions to investigate interactions between multiple modalities and tasks beyond visual and language modalities. This scalability makes POEM a versatile tool for different applications beyond prompt optimization .

  5. Expert Validation and Usability: Through expert interviews, POEM was validated for its effectiveness and usability by academic and industry researchers experienced in prompt engineering and multimodal LLM training. Experts appreciated the system's workflow, design, and interaction features, highlighting its efficiency in prompt iteration and instance-level evaluation, which enhances user confidence in prompt development .

In summary, POEM stands out for its user-friendly design, efficient prompt iteration process, detailed multimodal reasoning pattern exploration, model-agnostic approach, and scalability for various multimodal tasks, making it a valuable tool for enhancing multimodal reasoning of Large Language Models .


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 enhancing multimodal reasoning of Large Language Models (LLMs). Noteworthy researchers in this field include P. P. Liang, Y. Cheng, R. Salakhutdinov, L.-P. Morency, S. Gehrmann, M. N. Hoque, W. He, A. K. Shekar, S. Huang, L. Dong, W. Wang, Y. Hao, S. Singhal, among others . These researchers have contributed to various aspects of multimodal interactions, fusion, visualization, and understanding of multimodal models.

The key to the solution mentioned in the paper "POEM: Interactive Prompt Optimization for Enhancing Multimodal Reasoning of Large Language Models" is the development of a visual analytics tool called POEM. This tool is designed to facilitate prompt engineering for improving the multimodal reasoning of LLMs by incorporating human insight and expertise. It allows users to assess prompt effectiveness, revise prompts efficiently, and apply their knowledge to enhance prompt iteration for targeted downstream tasks . The tool aims to empower model practitioners to align and enhance model performance with their expertise through prompting, thereby improving the overall reasoning capabilities of Large Language Models.


How were the experiments in the paper designed?

The experiments in the paper were designed through a systematic approach involving two case studies and expert interviews to validate the efficacy and efficiency of the POEM system . The experiments included introducing POEM, demonstrating its workflow and functions, allowing experts to explore the system using real datasets, and gathering feedback through semi-structured interviews with academic researchers and industry research scientists . The experts verified the effectiveness and usability of POEM, highlighting the thoughtful design of the workflow, the intuitive visual and interaction design, and the system's capability to examine and evaluate at the instance level with reference to raw data . The experiments aimed to assess prompt effectiveness, offer strategies for prompt revision, and enable users to apply their knowledge for efficient prompt iteration .


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

The dataset used for quantitative evaluation in the study is the CMU-MOSEI dataset for multimodal sentiment analysis . 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 strong support for the scientific hypotheses that needed verification. The study conducted tests to evaluate prompt robustness, achieving an accuracy of 75% with correctly predicted test instances . Additionally, the paper introduces POEM, a visual analytics tool designed to enhance prompt engineering for improving multimodal reasoning of Large Language Models (LLMs) with human insight and expertise. The system allows users to assess prompt effectiveness, revise prompts, and apply their knowledge for efficient prompt iteration, as validated through case studies and positive feedback from experts .

Furthermore, the research explores various aspects related to multimodal interactions, quantifying and modeling these interactions, as well as studying human and automatic quantification of multimodal fusion interactions . The experiments delve into semi-supervised multimodal learning, visualizing and understanding multimodal models, and quantifying interactions in these models . These analyses contribute to a comprehensive understanding of how multimodal reasoning operates within LLMs, supporting the scientific hypotheses and enhancing the knowledge base in this domain.


What are the contributions of this paper?

The paper introduces POEM, a visual analytics tool designed to enhance multimodal reasoning of Large Language Models (LLMs) through prompt engineering with human insight and expertise. The contributions of the paper include:

  • Introducing a novel visual analytics tool, POEM, that facilitates prompt engineering for enhancing multimodal reasoning of LLMs .
  • Allowing users to assess prompt effectiveness, summarize multimodal reasoning patterns, and provide strategies for prompt revision, enabling efficient prompt iteration .
  • Validating the system's efficacy and efficiency through two case studies and receiving positive feedback from experts .

What work can be continued in depth?

Further research can delve deeper into several aspects related to enhancing multimodal reasoning performance of Large Language Models (LLMs) based on the information provided in the context:

  • Reducing Hallucination: Future efforts can focus on developing techniques to reduce hallucination occurrences in model outputs, which is a current deficiency in Large Language Models .
  • Integration of Advanced Expert Models: There is a potential to integrate more advanced expert models to mitigate potential information loss or inaccuracies and visualize uncertainties to increase user trust .
  • Comparison Across Multiple LLMs: Extending the work to enable comparison across multiple LLMs can provide insights into effective prompt engineering strategies for different models and tasks .
  • Interaction Involving More Modalities: The research can be extended to study interaction involving more modalities in increasingly complex scenarios and applications to enhance multimodal reasoning performance .
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