First Multi-Dimensional Evaluation of Flowchart Comprehension for Multimodal Large Language Models

Enming Zhang, Ruobing Yao, Huanyong Liu, Junhui Yu, Jiale Wang·June 14, 2024

Summary

FlowCE is a comprehensive evaluation method introduced to assess the understanding of Multimodal Large Language Models (MLLMs) in flowchart-related tasks. It evaluates models in five key areas: reasoning, localization recognition, information extraction, logical verification, and summarization. Despite advancements in models like GPT-4 and Phi-3-Vision, their performance is limited, with GPT-4 achieving a score of 56.63 and Phi-3-Vision topping open-source models at 49.97. FlowCE fills a gap in the lack of a unified framework and aims to guide future research by providing a standardized benchmark for flowchart comprehension. The study also emphasizes the need for better model performance, particularly in tasks like information extraction and logical verification, where open-source models lag behind.

Key findings

12

Paper digest

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

The paper aims to address the evaluation of flowchart comprehension for Multimodal Large Language Models (MLLMs) by introducing a multi-dimensional evaluation framework . This evaluation framework assesses the cross-modal understanding capabilities of existing MLLMs between images and text through various benchmarks like MMBench, MME, TextVQA, and others . The paper focuses on tasks such as logical verification, summarization, information extraction, localization recognition, and reasoning in the context of flowchart comprehension . This problem of evaluating MLLMs' comprehension of flowcharts is a new and evolving area of research, as indicated by the emergence of various open-source efforts and benchmarks specifically tailored for this purpose .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis related to the evaluation of Multimodal Large Language Models (MLLMs) in tasks associated with flowcharts . The scientific hypothesis revolves around the comprehensive assessment of MLLMs across various dimensions for tasks concerning flowcharts, including Reasoning, Localization Recognition, Information Extraction, Logical Verification, and Summarization . The proposed method, FlowCE, is designed to evaluate the capabilities of MLLMs in understanding and analyzing flowcharts, addressing the need for a systematic evaluation approach in this domain .


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

The paper introduces several new ideas, methods, and models in the field of multimodal large language models (MLLMs) based on the details provided:

  • Evaluation Methods: The paper presents a multi-dimensional evaluation framework for assessing the comprehension of flowcharts by MLLMs, focusing on tasks like reasoning, localization recognition, and summarization .
  • Model Comparison: It compares various MLLMs, both proprietary and open-source, with different parameter sizes ranging from 3.4B to 13B and above, highlighting the performance differences based on model parameters and data volumes .
  • FlowchartQA Benchmark: Introduces the FlowchartQA benchmark, a large-scale benchmark for reasoning over flowcharts, aiming to evaluate the reasoning capabilities of MLLMs in interpreting flowchart information .
  • Visual Expert Models: The paper discusses models like Cogvlm and LLAVA, which are visual experts for pretrained language models, focusing on understanding, localization, text reading, and other tasks .
  • Innovative Training Strategies: It explores innovative training strategies for MLLMs, such as instruction tuning, to enhance model performance in tasks involving visual and textual data .
  • Dataset Quality Impact: Emphasizes the importance of dataset quality and diversity in model performance, showcasing how models like Phi-3-Vision leverage high-quality and diverse datasets to achieve superior scores in information extraction tasks .
  • Detailed Benchmarking: Provides detailed benchmarking results for different MLLMs, highlighting their performance in tasks like reasoning, summarization, and localization recognition, offering insights into the strengths and weaknesses of each model .

These contributions collectively advance the understanding and evaluation of MLLMs in processing multimodal data, offering insights into their capabilities, limitations, and potential for future development in the field. The paper introduces several novel characteristics and advantages compared to previous methods in the field of multimodal large language models (MLLMs) based on the provided details:

  • Evaluation Framework: The paper presents a multi-dimensional evaluation framework for assessing flowchart comprehension by MLLMs, focusing on tasks like reasoning, localization recognition, and summarization .
  • Model Comparison: It compares various MLLMs with different parameter sizes, ranging from 3.4B to 13B and above, to highlight performance variances based on model parameters and data volumes .
  • FlowchartQA Benchmark: Introduces the FlowchartQA benchmark, a large-scale benchmark specifically designed to evaluate the reasoning capabilities of MLLMs in interpreting flowchart information .
  • Visual Expert Models: Discusses models like Cogvlm and LLAVA, which serve as visual experts for pretrained language models, focusing on tasks like understanding, localization, and text reading .
  • Innovative Training Strategies: Explores innovative training strategies such as instruction tuning to enhance MLLM performance in tasks involving visual and textual data .
  • Dataset Quality Impact: Emphasizes the significance of dataset quality and diversity in influencing model performance, showcasing how models like Phi-3-Vision leverage high-quality and diverse datasets to achieve superior scores in information extraction tasks .
  • Detailed Benchmarking: Provides detailed benchmarking results for different MLLMs, offering insights into their performance in tasks like reasoning, summarization, and localization recognition, thereby highlighting the strengths and weaknesses of each model .

These characteristics and advancements collectively contribute to enhancing the understanding and evaluation of MLLMs in processing multimodal data, paving the way for improved model development and performance in various 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?

Several related research efforts exist in the field of multimodal large language models (MLLMs) and flowchart comprehension. Noteworthy researchers in this area include Haotian Liu, Chunyuan Li, Yuheng Li, Yong Jae Lee, Marah Abdin, Sam Ade Jacobs, and many others . These researchers have contributed to advancements in evaluating the cross-modal understanding capabilities of MLLMs between images and text, as well as developing benchmarks like MMBench, TextVQA, and ChartQA .

The key to the solution mentioned in the paper involves conducting experiments on existing mainstream MLLMs, both proprietary and open-source models, with a focus on semantic similarity between standard answers and MLLM model outputs. The evaluations are based on a protocol that includes tasks such as reasoning, localization recognition, summarization, information extraction, and logical verification . The experiments aim to assess the performance of different MLLMs in various tasks and dimensions, providing insights into their capabilities and effectiveness in understanding flowcharts and generating content .


How were the experiments in the paper designed?

The experiments in the paper were designed to comprehensively evaluate Multimodal Large Language Models (MLLMs) using a method called FlowCE across various dimensions for tasks related to flowcharts. These dimensions include Reasoning, Localization Recognition, Information Extraction, Logical Verification, and Summarization on flowcharts . The evaluation involved diverse question-answer pairs for different tasks in open environments, utilizing flowchart images from real-world scenarios and styles . Additionally, the evaluation method included tasks such as open-ended question answering, reasoning, localization recognition, and summarization, with the use of GPT4 to assess semantic similarity between standard answers and responses generated by MLLMs . The experiments aimed to assess the understanding capabilities of MLLMs on flowcharts and provide insights into their strengths and limitations in interpreting flowchart information .


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

The dataset used for quantitative evaluation in the study is called FlowCE, which sets up five major categories of tasks to thoroughly quantify the understanding capability and performance of Multimodal Large Language Models (MLLMs) on flowcharts . The study extensively evaluates mainstream MLLMs, both open-source and proprietary, using the FlowCE framework . The code for the evaluation methodology and tasks across different dimensions in FlowCE is open source, as the authors mention open-sourcing their resources to foster future advancements in the field .


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 require verification. The study conducted a detailed comparison of various models, including GPT4o, Phi-3-Vision, LLaVA-Next-Vicuna-13B, and others, on tasks like logical verification, summarization, information extraction, localization recognition, and reasoning . The evaluation results showcased the performance of these multimodal large language models across different dimensions of tasks, with GPT4o standing out with the highest accuracy score of 83.81 in aligning predictions with actual labels . This indicates robust performance in logical verification tasks.

Furthermore, the paper extensively evaluated open-source and commercial multimodal large language models at different parameter levels, highlighting the advancements and challenges in these models . Despite significant progress in multimodal large language models, the study revealed that these models still struggle to demonstrate consistent performance across all categories . Factors such as training data bias, model overfitting, and algorithmic tuning were identified as contributors to models exhibiting a high probability of providing affirmative responses .

The detailed comparison of model outputs and received scores for different tasks, as shown in the figures, provides a clear insight into the performance variations among the models . The evaluation methodology employed, including semantic similarity assessments and accuracy calculations using GPT4, ensured a systematic approach to quantifying the model outputs . This rigorous evaluation method enhances the credibility of the study's findings and supports the scientific hypotheses being tested.

In conclusion, the experiments and results presented in the paper offer strong support for the scientific hypotheses that needed verification. The comprehensive evaluation of multimodal large language models across various tasks, coupled with detailed comparisons and performance metrics, contributes significantly to the understanding of the capabilities and limitations of these models in real-world applications .


What are the contributions of this paper?

The paper makes significant contributions by proposing the first comprehensive method, FlowCE, to assess Multimodal Large Language Models (MLLMs) across various dimensions for tasks related to flowcharts. It evaluates MLLMs' abilities in Reasoning, Localization Recognition, Information Extraction, Logical Verification, and Summarization on flowcharts . The evaluation results of MLLMs models at different parameter levels and mainstream commercial models are extensively presented in the paper . Additionally, the paper highlights the performance of different MLLMs models in tasks related to flowcharts, providing insights into their capabilities and limitations .


What work can be continued in depth?

To delve deeper into the evaluation of Multimodal Large Language Models (MLLMs), further exploration can focus on the following aspects:

  • Evaluation Methodologies: Understanding the detailed evaluation methodologies used for different tasks across various dimensions, such as reasoning, information extraction, localization recognition, summarization, and logical verification .
  • Benchmark Comparison: Conducting a comparative analysis of FlowCE with existing benchmarks to assess the unique contributions and comprehensive evaluation capabilities of FlowCE in understanding flowcharts .
  • Real-World Data Creation: Creating real-world flowchart data and open-scenario question-answer pairs to enhance the authenticity and applicability of evaluations .
  • Task Expansion: Expanding tasks across more dimensions beyond existing benchmarks like FlowchartQA to provide a broader assessment of MLLMs' abilities in diverse scenarios .
  • Manual Annotation Challenges: Addressing the challenges associated with manual annotation in data generation and exploring strategies to minimize errors as datasets grow .
  • Model Performance Enhancement: Investigating methods to enhance the performance of MLLMs through fine-tuning processes like instruction tuning and leveraging visual and textual data for pre-training .
  • Model Capabilities: Assessing the cross-modal understanding capabilities of MLLMs between images and text through various benchmarks like MMBench, MME, TextVQA, and others .
  • Task-Specific Assessments: Conducting detailed assessments of model capabilities in domain-specific tasks like TextVQA, DocVQA, MathVista, ChartQA, and InfographicQA to evaluate specific competencies of MLLMs .
  • Performance Challenges: Addressing the existing challenges faced by MLLMs in tasks like reasoning, summarization, information extraction, and localization recognition to enhance overall performance .

Introduction
Background
Emergence of MLLMs and their limitations
Importance of flowcharts in various domains
Lack of a unified benchmark for flowchart understanding
Objective
To introduce FlowCE as a standardized evaluation tool
To assess current MLLM performance in flowchart tasks
To identify areas for future research and improvement
Method
Data Collection
Selection of diverse flowchart datasets
Source and creation of synthetic flowchart data
Benchmark datasets comparison
Data Preprocessing
Standardization of flowchart formats
Annotation and labeling of key tasks
Splitting datasets for training, validation, and testing
Flowchart Reasoning
Task definition and evaluation metrics
Analysis of model performance on reasoning tasks
Localization and Recognition
Image-to-text and text-to-image matching
Evaluation of model's ability to identify flowchart elements
Information Extraction
Identifying key steps and connections
Assessing models' capacity for extracting relevant data
Logical Verification
Evaluating model's understanding of flowchart logic
Testing for consistency and correctness in reasoning
Summarization
Assessing models' ability to condense flowchart content
Comparison with human-generated summaries
Results and Analysis
Performance comparison of MLLMs, including GPT-4 and Phi-3-Vision
Identification of strengths and weaknesses in current models
Discussion on the need for improvement in specific tasks
Conclusion
Significance of FlowCE as a benchmark for future research
Recommendations for model development and training
Implications for the advancement of flowchart-related AI applications
Basic info
papers
computer vision and pattern recognition
artificial intelligence
Advanced features
Insights
How does FlowCE contribute to the evaluation of MLLMs in flowchart-related tasks?
What is FlowCE used for?
What are the five key areas assessed by FlowCE?
Which models are mentioned for comparison in the context of FlowCE?

First Multi-Dimensional Evaluation of Flowchart Comprehension for Multimodal Large Language Models

Enming Zhang, Ruobing Yao, Huanyong Liu, Junhui Yu, Jiale Wang·June 14, 2024

Summary

FlowCE is a comprehensive evaluation method introduced to assess the understanding of Multimodal Large Language Models (MLLMs) in flowchart-related tasks. It evaluates models in five key areas: reasoning, localization recognition, information extraction, logical verification, and summarization. Despite advancements in models like GPT-4 and Phi-3-Vision, their performance is limited, with GPT-4 achieving a score of 56.63 and Phi-3-Vision topping open-source models at 49.97. FlowCE fills a gap in the lack of a unified framework and aims to guide future research by providing a standardized benchmark for flowchart comprehension. The study also emphasizes the need for better model performance, particularly in tasks like information extraction and logical verification, where open-source models lag behind.
Mind map
Comparison with human-generated summaries
Assessing models' ability to condense flowchart content
Testing for consistency and correctness in reasoning
Evaluating model's understanding of flowchart logic
Assessing models' capacity for extracting relevant data
Identifying key steps and connections
Evaluation of model's ability to identify flowchart elements
Image-to-text and text-to-image matching
Analysis of model performance on reasoning tasks
Task definition and evaluation metrics
Summarization
Logical Verification
Information Extraction
Localization and Recognition
Flowchart Reasoning
Benchmark datasets comparison
Source and creation of synthetic flowchart data
Selection of diverse flowchart datasets
To identify areas for future research and improvement
To assess current MLLM performance in flowchart tasks
To introduce FlowCE as a standardized evaluation tool
Lack of a unified benchmark for flowchart understanding
Importance of flowcharts in various domains
Emergence of MLLMs and their limitations
Implications for the advancement of flowchart-related AI applications
Recommendations for model development and training
Significance of FlowCE as a benchmark for future research
Discussion on the need for improvement in specific tasks
Identification of strengths and weaknesses in current models
Performance comparison of MLLMs, including GPT-4 and Phi-3-Vision
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Results and Analysis
Method
Introduction
Outline
Introduction
Background
Emergence of MLLMs and their limitations
Importance of flowcharts in various domains
Lack of a unified benchmark for flowchart understanding
Objective
To introduce FlowCE as a standardized evaluation tool
To assess current MLLM performance in flowchart tasks
To identify areas for future research and improvement
Method
Data Collection
Selection of diverse flowchart datasets
Source and creation of synthetic flowchart data
Benchmark datasets comparison
Data Preprocessing
Standardization of flowchart formats
Annotation and labeling of key tasks
Splitting datasets for training, validation, and testing
Flowchart Reasoning
Task definition and evaluation metrics
Analysis of model performance on reasoning tasks
Localization and Recognition
Image-to-text and text-to-image matching
Evaluation of model's ability to identify flowchart elements
Information Extraction
Identifying key steps and connections
Assessing models' capacity for extracting relevant data
Logical Verification
Evaluating model's understanding of flowchart logic
Testing for consistency and correctness in reasoning
Summarization
Assessing models' ability to condense flowchart content
Comparison with human-generated summaries
Results and Analysis
Performance comparison of MLLMs, including GPT-4 and Phi-3-Vision
Identification of strengths and weaknesses in current models
Discussion on the need for improvement in specific tasks
Conclusion
Significance of FlowCE as a benchmark for future research
Recommendations for model development and training
Implications for the advancement of flowchart-related AI applications
Key findings
12

Paper digest

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

The paper aims to address the evaluation of flowchart comprehension for Multimodal Large Language Models (MLLMs) by introducing a multi-dimensional evaluation framework . This evaluation framework assesses the cross-modal understanding capabilities of existing MLLMs between images and text through various benchmarks like MMBench, MME, TextVQA, and others . The paper focuses on tasks such as logical verification, summarization, information extraction, localization recognition, and reasoning in the context of flowchart comprehension . This problem of evaluating MLLMs' comprehension of flowcharts is a new and evolving area of research, as indicated by the emergence of various open-source efforts and benchmarks specifically tailored for this purpose .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis related to the evaluation of Multimodal Large Language Models (MLLMs) in tasks associated with flowcharts . The scientific hypothesis revolves around the comprehensive assessment of MLLMs across various dimensions for tasks concerning flowcharts, including Reasoning, Localization Recognition, Information Extraction, Logical Verification, and Summarization . The proposed method, FlowCE, is designed to evaluate the capabilities of MLLMs in understanding and analyzing flowcharts, addressing the need for a systematic evaluation approach in this domain .


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

The paper introduces several new ideas, methods, and models in the field of multimodal large language models (MLLMs) based on the details provided:

  • Evaluation Methods: The paper presents a multi-dimensional evaluation framework for assessing the comprehension of flowcharts by MLLMs, focusing on tasks like reasoning, localization recognition, and summarization .
  • Model Comparison: It compares various MLLMs, both proprietary and open-source, with different parameter sizes ranging from 3.4B to 13B and above, highlighting the performance differences based on model parameters and data volumes .
  • FlowchartQA Benchmark: Introduces the FlowchartQA benchmark, a large-scale benchmark for reasoning over flowcharts, aiming to evaluate the reasoning capabilities of MLLMs in interpreting flowchart information .
  • Visual Expert Models: The paper discusses models like Cogvlm and LLAVA, which are visual experts for pretrained language models, focusing on understanding, localization, text reading, and other tasks .
  • Innovative Training Strategies: It explores innovative training strategies for MLLMs, such as instruction tuning, to enhance model performance in tasks involving visual and textual data .
  • Dataset Quality Impact: Emphasizes the importance of dataset quality and diversity in model performance, showcasing how models like Phi-3-Vision leverage high-quality and diverse datasets to achieve superior scores in information extraction tasks .
  • Detailed Benchmarking: Provides detailed benchmarking results for different MLLMs, highlighting their performance in tasks like reasoning, summarization, and localization recognition, offering insights into the strengths and weaknesses of each model .

These contributions collectively advance the understanding and evaluation of MLLMs in processing multimodal data, offering insights into their capabilities, limitations, and potential for future development in the field. The paper introduces several novel characteristics and advantages compared to previous methods in the field of multimodal large language models (MLLMs) based on the provided details:

  • Evaluation Framework: The paper presents a multi-dimensional evaluation framework for assessing flowchart comprehension by MLLMs, focusing on tasks like reasoning, localization recognition, and summarization .
  • Model Comparison: It compares various MLLMs with different parameter sizes, ranging from 3.4B to 13B and above, to highlight performance variances based on model parameters and data volumes .
  • FlowchartQA Benchmark: Introduces the FlowchartQA benchmark, a large-scale benchmark specifically designed to evaluate the reasoning capabilities of MLLMs in interpreting flowchart information .
  • Visual Expert Models: Discusses models like Cogvlm and LLAVA, which serve as visual experts for pretrained language models, focusing on tasks like understanding, localization, and text reading .
  • Innovative Training Strategies: Explores innovative training strategies such as instruction tuning to enhance MLLM performance in tasks involving visual and textual data .
  • Dataset Quality Impact: Emphasizes the significance of dataset quality and diversity in influencing model performance, showcasing how models like Phi-3-Vision leverage high-quality and diverse datasets to achieve superior scores in information extraction tasks .
  • Detailed Benchmarking: Provides detailed benchmarking results for different MLLMs, offering insights into their performance in tasks like reasoning, summarization, and localization recognition, thereby highlighting the strengths and weaknesses of each model .

These characteristics and advancements collectively contribute to enhancing the understanding and evaluation of MLLMs in processing multimodal data, paving the way for improved model development and performance in various 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?

Several related research efforts exist in the field of multimodal large language models (MLLMs) and flowchart comprehension. Noteworthy researchers in this area include Haotian Liu, Chunyuan Li, Yuheng Li, Yong Jae Lee, Marah Abdin, Sam Ade Jacobs, and many others . These researchers have contributed to advancements in evaluating the cross-modal understanding capabilities of MLLMs between images and text, as well as developing benchmarks like MMBench, TextVQA, and ChartQA .

The key to the solution mentioned in the paper involves conducting experiments on existing mainstream MLLMs, both proprietary and open-source models, with a focus on semantic similarity between standard answers and MLLM model outputs. The evaluations are based on a protocol that includes tasks such as reasoning, localization recognition, summarization, information extraction, and logical verification . The experiments aim to assess the performance of different MLLMs in various tasks and dimensions, providing insights into their capabilities and effectiveness in understanding flowcharts and generating content .


How were the experiments in the paper designed?

The experiments in the paper were designed to comprehensively evaluate Multimodal Large Language Models (MLLMs) using a method called FlowCE across various dimensions for tasks related to flowcharts. These dimensions include Reasoning, Localization Recognition, Information Extraction, Logical Verification, and Summarization on flowcharts . The evaluation involved diverse question-answer pairs for different tasks in open environments, utilizing flowchart images from real-world scenarios and styles . Additionally, the evaluation method included tasks such as open-ended question answering, reasoning, localization recognition, and summarization, with the use of GPT4 to assess semantic similarity between standard answers and responses generated by MLLMs . The experiments aimed to assess the understanding capabilities of MLLMs on flowcharts and provide insights into their strengths and limitations in interpreting flowchart information .


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

The dataset used for quantitative evaluation in the study is called FlowCE, which sets up five major categories of tasks to thoroughly quantify the understanding capability and performance of Multimodal Large Language Models (MLLMs) on flowcharts . The study extensively evaluates mainstream MLLMs, both open-source and proprietary, using the FlowCE framework . The code for the evaluation methodology and tasks across different dimensions in FlowCE is open source, as the authors mention open-sourcing their resources to foster future advancements in the field .


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 require verification. The study conducted a detailed comparison of various models, including GPT4o, Phi-3-Vision, LLaVA-Next-Vicuna-13B, and others, on tasks like logical verification, summarization, information extraction, localization recognition, and reasoning . The evaluation results showcased the performance of these multimodal large language models across different dimensions of tasks, with GPT4o standing out with the highest accuracy score of 83.81 in aligning predictions with actual labels . This indicates robust performance in logical verification tasks.

Furthermore, the paper extensively evaluated open-source and commercial multimodal large language models at different parameter levels, highlighting the advancements and challenges in these models . Despite significant progress in multimodal large language models, the study revealed that these models still struggle to demonstrate consistent performance across all categories . Factors such as training data bias, model overfitting, and algorithmic tuning were identified as contributors to models exhibiting a high probability of providing affirmative responses .

The detailed comparison of model outputs and received scores for different tasks, as shown in the figures, provides a clear insight into the performance variations among the models . The evaluation methodology employed, including semantic similarity assessments and accuracy calculations using GPT4, ensured a systematic approach to quantifying the model outputs . This rigorous evaluation method enhances the credibility of the study's findings and supports the scientific hypotheses being tested.

In conclusion, the experiments and results presented in the paper offer strong support for the scientific hypotheses that needed verification. The comprehensive evaluation of multimodal large language models across various tasks, coupled with detailed comparisons and performance metrics, contributes significantly to the understanding of the capabilities and limitations of these models in real-world applications .


What are the contributions of this paper?

The paper makes significant contributions by proposing the first comprehensive method, FlowCE, to assess Multimodal Large Language Models (MLLMs) across various dimensions for tasks related to flowcharts. It evaluates MLLMs' abilities in Reasoning, Localization Recognition, Information Extraction, Logical Verification, and Summarization on flowcharts . The evaluation results of MLLMs models at different parameter levels and mainstream commercial models are extensively presented in the paper . Additionally, the paper highlights the performance of different MLLMs models in tasks related to flowcharts, providing insights into their capabilities and limitations .


What work can be continued in depth?

To delve deeper into the evaluation of Multimodal Large Language Models (MLLMs), further exploration can focus on the following aspects:

  • Evaluation Methodologies: Understanding the detailed evaluation methodologies used for different tasks across various dimensions, such as reasoning, information extraction, localization recognition, summarization, and logical verification .
  • Benchmark Comparison: Conducting a comparative analysis of FlowCE with existing benchmarks to assess the unique contributions and comprehensive evaluation capabilities of FlowCE in understanding flowcharts .
  • Real-World Data Creation: Creating real-world flowchart data and open-scenario question-answer pairs to enhance the authenticity and applicability of evaluations .
  • Task Expansion: Expanding tasks across more dimensions beyond existing benchmarks like FlowchartQA to provide a broader assessment of MLLMs' abilities in diverse scenarios .
  • Manual Annotation Challenges: Addressing the challenges associated with manual annotation in data generation and exploring strategies to minimize errors as datasets grow .
  • Model Performance Enhancement: Investigating methods to enhance the performance of MLLMs through fine-tuning processes like instruction tuning and leveraging visual and textual data for pre-training .
  • Model Capabilities: Assessing the cross-modal understanding capabilities of MLLMs between images and text through various benchmarks like MMBench, MME, TextVQA, and others .
  • Task-Specific Assessments: Conducting detailed assessments of model capabilities in domain-specific tasks like TextVQA, DocVQA, MathVista, ChartQA, and InfographicQA to evaluate specific competencies of MLLMs .
  • Performance Challenges: Addressing the existing challenges faced by MLLMs in tasks like reasoning, summarization, information extraction, and localization recognition to enhance overall performance .
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