Multimodal LLMs Struggle with Basic Visual Network Analysis: a VNA Benchmark

Evan M. Williams, Kathleen M. Carley·May 10, 2024

Summary

The paper evaluates the zero-shot performance of GPT-4 and LLaVa, two large language and vision models, on Visual Network Analysis (VNA) tasks, specifically focusing on degree, structural balance, and component analysis. GPT-4 outperforms LLaVa, but both models exhibit difficulty in these basic tasks, which require understanding of graph theory. The authors introduce a VNA benchmark with five tasks and contribute to the need for improvement in LLMs' and VLMs' performance in network analysis. The studies highlight the models' limitations, with GPT-4 performing better on some tasks but struggling with others, and emphasize the importance of further research in fine-tuning and prompt engineering for better graph-related task performance. Concurrently, other datasets and benchmarks, like VisionGraph Benchmark and GITQA Dataset, are also mentioned, indicating the growing interest in evaluating multimodal models in graph 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 challenge of multimodal large language models (LLMs) struggling with basic visual network analysis (VNA) tasks, specifically focusing on tasks related to graph theory problems in a visual context . This paper introduces a Visual Network Analysis (VNA) Benchmark to evaluate the performance of Very Large Models (VLMs) on complex graph tasks such as cycle identification, shortest path identification, and maximum flow . While the specific tasks evaluated in the paper are new in the context of assessing LLMs and VLMs on visual graph analysis, the broader issue of model performance on graph-related tasks has been a subject of recent interest and research .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis related to the performance of Large Language Models (LLMs) in learning on graphs, specifically focusing on exploring the potential of LLMs in graph-related tasks . The study delves into the challenges faced by multimodal LLMs in basic visual network analysis, particularly in tasks such as structural balance, component analysis, and isolate counting . The research investigates the capabilities and limitations of LLMs in handling graph theory problems in a visual context, aiming to assess their effectiveness in tasks like graph reasoning and object classification .


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

The paper "Multimodal LLMs Struggle with Basic Visual Network Analysis: a VNA Benchmark" introduces several new ideas, methods, and models related to large language models (LLMs) and visual network analysis . Here are some key points from the paper:

  1. Exploration of Large Language Models (LLMs) in Learning on Graphs: The paper explores the potential of LLMs in learning on graphs, focusing on tasks related to graph theory problems in a visual context .

  2. Graph-Image-Text Question Answering (GITQA) Dataset: Wei et al. introduced a GITQA Dataset along with an end-to-end framework for general graph reasoning, which involves encoding graphs for large language models .

  3. Zero-Shot Object Classification: The paper delves into zero-shot object classification using large multimodal models, pushing boundaries in object classification tasks without prior training data .

  4. Multimodal Neurons in Artificial Neural Networks: Goh et al. discuss the concept of multimodal neurons in artificial neural networks, highlighting the importance of multimodal capabilities in neural network architectures .

  5. Visiongraph Model: Li et al. propose the Visiongraph model, leveraging large multimodal models for solving graph theory problems in a visual context, aiming to enhance graph reasoning in LLMs .

  6. Large Vision Language Models (VLMs): The paper mentions a surge in interest in evaluating Large Vision Language Models (VLMs) for various computer vision tasks, emphasizing the application of VLMs in computer vision .

  7. Structural Balance Task: The paper discusses the concept of structural balance in social network analysis, focusing on the balance theory and its application to group evaluations in signed graphs .

  8. Component Tasks: The paper covers component tasks related to connected graphs or subgraphs, highlighting the evaluation of components and isolates in graphs, with a focus on human readability and visual layouts .

These ideas, methods, and models presented in the paper contribute to advancing the understanding and application of large language models in the context of visual network analysis, graph reasoning, and social network analysis. The paper "Multimodal LLMs Struggle with Basic Visual Network Analysis: a VNA Benchmark" introduces novel characteristics and advantages compared to previous methods in the field of large language models (LLMs) and visual network analysis .

  1. Structural Balance Task:

    • Characteristics: The paper delves into the Structural Balance Task, focusing on the concept of structural balance in social network analysis, where the balance theory plays a crucial role in evaluating relationships within a group .
    • Advantages: By exploring structural balance in signed graphs, the paper provides a detailed understanding of how cognitive balance and group evaluations influence the overall balance within social networks, offering insights into the dynamics of relationships .
  2. Component Tasks:

    • Characteristics: The paper discusses component tasks related to connected graphs or subgraphs, emphasizing the identification of components and isolates in graphs .
    • Advantages: Through the analysis of components and isolates, the paper enhances human readability and visual layouts in graph analysis, providing a comprehensive view of graph structures and connectivity .
  3. Zero-Shot Object Classification:

    • Characteristics: The paper explores zero-shot object classification using large multimodal models, pushing boundaries in object classification tasks without prior training data .
    • Advantages: By focusing on zero-shot object classification, the paper advances the understanding of how large multimodal models can excel in tasks such as differentiating animals, counting animals, identifying written digits, and identifying objects .
  4. Multimodal Neurons in Artificial Neural Networks:

    • Characteristics: The paper discusses the concept of multimodal neurons in artificial neural networks, highlighting the importance of multimodal capabilities in neural network architectures .
    • Advantages: By exploring multimodal neurons, the paper contributes to enhancing the functionality and performance of artificial neural networks, paving the way for improved multimodal processing and analysis .
  5. Visiongraph Model:

    • Characteristics: The Visiongraph model proposed in the paper leverages large multimodal models for solving graph theory problems in a visual context .
    • Advantages: The Visiongraph model enhances graph reasoning in large language models by incorporating visual elements, thereby improving the understanding and analysis of graph structures in a visual context .

These characteristics and advantages presented in the paper contribute to the advancement of large language models in the context of visual network analysis, graph reasoning, and social network analysis, offering valuable insights and methodologies for future research and applications in the field.


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 works exist in the field of multimodal large language models (LLMs) and visual network analysis. Noteworthy researchers in this field include:

  • Li, Y., Hu, B., Shi, H., Wang, W., Wang, L., Zhang, M.
  • Liu, H., Li, C., Li, Y., Li, B., Zhang, Y., Shen, S., Lee, Y.J.
  • Wei, Y., Fu, S., Jiang, W., Kwok, J.T., Zhang, Y.
  • Xu, J., Le, H., Samaras, D.
  • Zeng, J., Huang, R., Malik, W., Yin, L., Babic, B., Shacham, D., Yan, X., Yang, J., He, Q.

The key to the solution mentioned in the paper "Multimodal LLMs Struggle with Basic Visual Network Analysis: a VNA Benchmark" involves creating tasks related to important graph theory concepts such as degree, structural balance, and components. For example, tasks include identifying the maximum degree of a graph, determining structural balance in triads, and counting components and isolates in graphs. The tasks are designed to be related to zero-shot object counting and require the multimodal LLMs to count specific elements within the graphs to solve the tasks effectively .


How were the experiments in the paper designed?

The experiments in the paper were designed by programatically generating graphs using Python libraries such as NetworkX and netgraph . These graphs were used for tasks related to degree centrality and structural balance . The experiments involved evaluating the performance of GPT-4 and LLaVa on tasks like identifying the maximum node degree in a graph and categorizing triads based on the number of edges present . The experiments also included tasks related to component analysis and isolate counting, where the models were assessed based on accuracy, Mean Absolute Error (MAE), and Mean Squared Error (MSE) metrics .


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

The dataset used for quantitative evaluation in the study is the VisionGraph Benchmark, which contains tasks evaluating VLM performance on various graph tasks such as cycle identification, identifying shortest paths, and identifying maximum flow . The code for the VisionGraph Benchmark is open source and publicly available on GitHub .


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 valuable insights into the performance of Large Language Models (LLMs) on various visual network analysis tasks. The study evaluates the capabilities of LLMs, specifically GPT-4 and LLaVa, on tasks such as identifying the maximum node degree in a graph, counting components and isolates, and determining structural balance in triads . The results indicate that both LLMs struggled on these tasks, with GPT-4 achieving an overall accuracy of 0.51 on the structural balance task, which is comparable to random guessing .

Despite the simplicity of the tasks, both LLMs faced challenges, particularly in tasks related to structural balance, where they exhibited inconsistent reasoning and faulty predictions, even when provided with clear definitions in the prompts . For example, LLaVa predicted that every triad was unbalanced when given a definition of structural balance, highlighting the difficulties faced by LLMs in understanding and applying graph theory concepts .

The study also highlights the limitations of LLMs in processing images as patches, which may contribute to their struggles in tasks involving graph analysis . The findings suggest that more research is needed to understand why LLMs face difficulties in tasks as simple as counting specific elements in graphs and to explore the performance of LLMs when fine-tuned on graph-related tasks .

In conclusion, while the experiments provide valuable insights into the performance of LLMs on visual network analysis tasks, the results indicate that there are challenges and limitations that need to be addressed to improve the effectiveness of LLMs in tasks related to graph theory and visual network analysis . Further research is necessary to enhance the performance of LLMs on these tasks and to explore the impact of different visualization parameters and prompt engineering on LLM evaluation in visual network analysis tasks .


What are the contributions of this paper?

The paper makes several key contributions:

  • It proposes the task of zero-shot Visual Network Analysis to assess the performance of Large Vision Language Models (LLMs) on graph analytics tasks, focusing on concepts like maximum degree, structural balance, and identifying components .
  • The benchmark created includes tasks related to core network science concepts and evaluates LLMs' ability to identify and count elements in graphs, which is crucial for network data analysis .
  • The research publicly releases all generated data and ground-truth labels, providing transparency and reproducibility in the evaluation process .

What work can be continued in depth?

Work that can be continued in depth typically involves projects or tasks that require further analysis, research, or development. This could include in-depth research studies, complex problem-solving initiatives, detailed data analysis, comprehensive strategic planning, or thorough process improvement efforts. Essentially, any work that requires a deep dive into the subject matter or requires a detailed and thorough approach can be continued in depth. If you have a specific area of work in mind, feel free to provide more details for a more tailored response.


Introduction
Background
Emergence of large language and vision models (LLMs and VLMs)
Importance of VNA tasks in understanding model capabilities
Objective
To assess GPT-4 and LLaVa's performance on VNA tasks
Highlight the need for improvement in network analysis capabilities
Method
Data Collection
Selection of Visual Network Analysis tasks: degree, structural balance, component analysis
Benchmark creation: VNA Benchmark with five tasks
Data Preprocessing
Preparing input data for zero-shot evaluation
Comparison of GPT-4 and LLaVa's input requirements
Model Evaluation
GPT-4 Performance
Zero-shot results on VNA tasks
Strengths and weaknesses in graph theory understanding
LLaVa Performance
Comparative analysis with GPT-4
Challenges faced by LLaVa in network analysis tasks
Limitations and Future Directions
Models' current limitations in handling graph-related tasks
Prompt engineering and fine-tuning implications
Concurrent research: VisionGraph Benchmark, GITQA Dataset
Conclusion
The importance of multimodal models in graph tasks
Call for enhanced performance in LLMs and VLMs for network analysis applications
Basic info
papers
computer vision and pattern recognition
computation and language
artificial intelligence
Advanced features
Insights
What are the main limitations of GPT-4 and LLaVa as observed in the paper?
Which model performs better in degree, structural balance, and component analysis, according to the evaluation?
What models does the paper compare for zero-shot performance in Visual Network Analysis tasks?
Why do the authors introduce a VNA benchmark, and what does it contribute to the field of LLMs and VLMs?

Multimodal LLMs Struggle with Basic Visual Network Analysis: a VNA Benchmark

Evan M. Williams, Kathleen M. Carley·May 10, 2024

Summary

The paper evaluates the zero-shot performance of GPT-4 and LLaVa, two large language and vision models, on Visual Network Analysis (VNA) tasks, specifically focusing on degree, structural balance, and component analysis. GPT-4 outperforms LLaVa, but both models exhibit difficulty in these basic tasks, which require understanding of graph theory. The authors introduce a VNA benchmark with five tasks and contribute to the need for improvement in LLMs' and VLMs' performance in network analysis. The studies highlight the models' limitations, with GPT-4 performing better on some tasks but struggling with others, and emphasize the importance of further research in fine-tuning and prompt engineering for better graph-related task performance. Concurrently, other datasets and benchmarks, like VisionGraph Benchmark and GITQA Dataset, are also mentioned, indicating the growing interest in evaluating multimodal models in graph tasks.
Mind map
Challenges faced by LLaVa in network analysis tasks
Comparative analysis with GPT-4
Strengths and weaknesses in graph theory understanding
Zero-shot results on VNA tasks
Comparison of GPT-4 and LLaVa's input requirements
Preparing input data for zero-shot evaluation
Benchmark creation: VNA Benchmark with five tasks
Selection of Visual Network Analysis tasks: degree, structural balance, component analysis
Highlight the need for improvement in network analysis capabilities
To assess GPT-4 and LLaVa's performance on VNA tasks
Importance of VNA tasks in understanding model capabilities
Emergence of large language and vision models (LLMs and VLMs)
Call for enhanced performance in LLMs and VLMs for network analysis applications
The importance of multimodal models in graph tasks
Concurrent research: VisionGraph Benchmark, GITQA Dataset
Prompt engineering and fine-tuning implications
Models' current limitations in handling graph-related tasks
LLaVa Performance
GPT-4 Performance
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Limitations and Future Directions
Model Evaluation
Method
Introduction
Outline
Introduction
Background
Emergence of large language and vision models (LLMs and VLMs)
Importance of VNA tasks in understanding model capabilities
Objective
To assess GPT-4 and LLaVa's performance on VNA tasks
Highlight the need for improvement in network analysis capabilities
Method
Data Collection
Selection of Visual Network Analysis tasks: degree, structural balance, component analysis
Benchmark creation: VNA Benchmark with five tasks
Data Preprocessing
Preparing input data for zero-shot evaluation
Comparison of GPT-4 and LLaVa's input requirements
Model Evaluation
GPT-4 Performance
Zero-shot results on VNA tasks
Strengths and weaknesses in graph theory understanding
LLaVa Performance
Comparative analysis with GPT-4
Challenges faced by LLaVa in network analysis tasks
Limitations and Future Directions
Models' current limitations in handling graph-related tasks
Prompt engineering and fine-tuning implications
Concurrent research: VisionGraph Benchmark, GITQA Dataset
Conclusion
The importance of multimodal models in graph tasks
Call for enhanced performance in LLMs and VLMs for network analysis applications
Key findings
4

Paper digest

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

The paper aims to address the challenge of multimodal large language models (LLMs) struggling with basic visual network analysis (VNA) tasks, specifically focusing on tasks related to graph theory problems in a visual context . This paper introduces a Visual Network Analysis (VNA) Benchmark to evaluate the performance of Very Large Models (VLMs) on complex graph tasks such as cycle identification, shortest path identification, and maximum flow . While the specific tasks evaluated in the paper are new in the context of assessing LLMs and VLMs on visual graph analysis, the broader issue of model performance on graph-related tasks has been a subject of recent interest and research .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis related to the performance of Large Language Models (LLMs) in learning on graphs, specifically focusing on exploring the potential of LLMs in graph-related tasks . The study delves into the challenges faced by multimodal LLMs in basic visual network analysis, particularly in tasks such as structural balance, component analysis, and isolate counting . The research investigates the capabilities and limitations of LLMs in handling graph theory problems in a visual context, aiming to assess their effectiveness in tasks like graph reasoning and object classification .


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

The paper "Multimodal LLMs Struggle with Basic Visual Network Analysis: a VNA Benchmark" introduces several new ideas, methods, and models related to large language models (LLMs) and visual network analysis . Here are some key points from the paper:

  1. Exploration of Large Language Models (LLMs) in Learning on Graphs: The paper explores the potential of LLMs in learning on graphs, focusing on tasks related to graph theory problems in a visual context .

  2. Graph-Image-Text Question Answering (GITQA) Dataset: Wei et al. introduced a GITQA Dataset along with an end-to-end framework for general graph reasoning, which involves encoding graphs for large language models .

  3. Zero-Shot Object Classification: The paper delves into zero-shot object classification using large multimodal models, pushing boundaries in object classification tasks without prior training data .

  4. Multimodal Neurons in Artificial Neural Networks: Goh et al. discuss the concept of multimodal neurons in artificial neural networks, highlighting the importance of multimodal capabilities in neural network architectures .

  5. Visiongraph Model: Li et al. propose the Visiongraph model, leveraging large multimodal models for solving graph theory problems in a visual context, aiming to enhance graph reasoning in LLMs .

  6. Large Vision Language Models (VLMs): The paper mentions a surge in interest in evaluating Large Vision Language Models (VLMs) for various computer vision tasks, emphasizing the application of VLMs in computer vision .

  7. Structural Balance Task: The paper discusses the concept of structural balance in social network analysis, focusing on the balance theory and its application to group evaluations in signed graphs .

  8. Component Tasks: The paper covers component tasks related to connected graphs or subgraphs, highlighting the evaluation of components and isolates in graphs, with a focus on human readability and visual layouts .

These ideas, methods, and models presented in the paper contribute to advancing the understanding and application of large language models in the context of visual network analysis, graph reasoning, and social network analysis. The paper "Multimodal LLMs Struggle with Basic Visual Network Analysis: a VNA Benchmark" introduces novel characteristics and advantages compared to previous methods in the field of large language models (LLMs) and visual network analysis .

  1. Structural Balance Task:

    • Characteristics: The paper delves into the Structural Balance Task, focusing on the concept of structural balance in social network analysis, where the balance theory plays a crucial role in evaluating relationships within a group .
    • Advantages: By exploring structural balance in signed graphs, the paper provides a detailed understanding of how cognitive balance and group evaluations influence the overall balance within social networks, offering insights into the dynamics of relationships .
  2. Component Tasks:

    • Characteristics: The paper discusses component tasks related to connected graphs or subgraphs, emphasizing the identification of components and isolates in graphs .
    • Advantages: Through the analysis of components and isolates, the paper enhances human readability and visual layouts in graph analysis, providing a comprehensive view of graph structures and connectivity .
  3. Zero-Shot Object Classification:

    • Characteristics: The paper explores zero-shot object classification using large multimodal models, pushing boundaries in object classification tasks without prior training data .
    • Advantages: By focusing on zero-shot object classification, the paper advances the understanding of how large multimodal models can excel in tasks such as differentiating animals, counting animals, identifying written digits, and identifying objects .
  4. Multimodal Neurons in Artificial Neural Networks:

    • Characteristics: The paper discusses the concept of multimodal neurons in artificial neural networks, highlighting the importance of multimodal capabilities in neural network architectures .
    • Advantages: By exploring multimodal neurons, the paper contributes to enhancing the functionality and performance of artificial neural networks, paving the way for improved multimodal processing and analysis .
  5. Visiongraph Model:

    • Characteristics: The Visiongraph model proposed in the paper leverages large multimodal models for solving graph theory problems in a visual context .
    • Advantages: The Visiongraph model enhances graph reasoning in large language models by incorporating visual elements, thereby improving the understanding and analysis of graph structures in a visual context .

These characteristics and advantages presented in the paper contribute to the advancement of large language models in the context of visual network analysis, graph reasoning, and social network analysis, offering valuable insights and methodologies for future research and applications in the field.


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 works exist in the field of multimodal large language models (LLMs) and visual network analysis. Noteworthy researchers in this field include:

  • Li, Y., Hu, B., Shi, H., Wang, W., Wang, L., Zhang, M.
  • Liu, H., Li, C., Li, Y., Li, B., Zhang, Y., Shen, S., Lee, Y.J.
  • Wei, Y., Fu, S., Jiang, W., Kwok, J.T., Zhang, Y.
  • Xu, J., Le, H., Samaras, D.
  • Zeng, J., Huang, R., Malik, W., Yin, L., Babic, B., Shacham, D., Yan, X., Yang, J., He, Q.

The key to the solution mentioned in the paper "Multimodal LLMs Struggle with Basic Visual Network Analysis: a VNA Benchmark" involves creating tasks related to important graph theory concepts such as degree, structural balance, and components. For example, tasks include identifying the maximum degree of a graph, determining structural balance in triads, and counting components and isolates in graphs. The tasks are designed to be related to zero-shot object counting and require the multimodal LLMs to count specific elements within the graphs to solve the tasks effectively .


How were the experiments in the paper designed?

The experiments in the paper were designed by programatically generating graphs using Python libraries such as NetworkX and netgraph . These graphs were used for tasks related to degree centrality and structural balance . The experiments involved evaluating the performance of GPT-4 and LLaVa on tasks like identifying the maximum node degree in a graph and categorizing triads based on the number of edges present . The experiments also included tasks related to component analysis and isolate counting, where the models were assessed based on accuracy, Mean Absolute Error (MAE), and Mean Squared Error (MSE) metrics .


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

The dataset used for quantitative evaluation in the study is the VisionGraph Benchmark, which contains tasks evaluating VLM performance on various graph tasks such as cycle identification, identifying shortest paths, and identifying maximum flow . The code for the VisionGraph Benchmark is open source and publicly available on GitHub .


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 valuable insights into the performance of Large Language Models (LLMs) on various visual network analysis tasks. The study evaluates the capabilities of LLMs, specifically GPT-4 and LLaVa, on tasks such as identifying the maximum node degree in a graph, counting components and isolates, and determining structural balance in triads . The results indicate that both LLMs struggled on these tasks, with GPT-4 achieving an overall accuracy of 0.51 on the structural balance task, which is comparable to random guessing .

Despite the simplicity of the tasks, both LLMs faced challenges, particularly in tasks related to structural balance, where they exhibited inconsistent reasoning and faulty predictions, even when provided with clear definitions in the prompts . For example, LLaVa predicted that every triad was unbalanced when given a definition of structural balance, highlighting the difficulties faced by LLMs in understanding and applying graph theory concepts .

The study also highlights the limitations of LLMs in processing images as patches, which may contribute to their struggles in tasks involving graph analysis . The findings suggest that more research is needed to understand why LLMs face difficulties in tasks as simple as counting specific elements in graphs and to explore the performance of LLMs when fine-tuned on graph-related tasks .

In conclusion, while the experiments provide valuable insights into the performance of LLMs on visual network analysis tasks, the results indicate that there are challenges and limitations that need to be addressed to improve the effectiveness of LLMs in tasks related to graph theory and visual network analysis . Further research is necessary to enhance the performance of LLMs on these tasks and to explore the impact of different visualization parameters and prompt engineering on LLM evaluation in visual network analysis tasks .


What are the contributions of this paper?

The paper makes several key contributions:

  • It proposes the task of zero-shot Visual Network Analysis to assess the performance of Large Vision Language Models (LLMs) on graph analytics tasks, focusing on concepts like maximum degree, structural balance, and identifying components .
  • The benchmark created includes tasks related to core network science concepts and evaluates LLMs' ability to identify and count elements in graphs, which is crucial for network data analysis .
  • The research publicly releases all generated data and ground-truth labels, providing transparency and reproducibility in the evaluation process .

What work can be continued in depth?

Work that can be continued in depth typically involves projects or tasks that require further analysis, research, or development. This could include in-depth research studies, complex problem-solving initiatives, detailed data analysis, comprehensive strategic planning, or thorough process improvement efforts. Essentially, any work that requires a deep dive into the subject matter or requires a detailed and thorough approach can be continued in depth. If you have a specific area of work in mind, feel free to provide more details for a more tailored response.

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