MuirBench: A Comprehensive Benchmark for Robust Multi-image Understanding

Fei Wang, Xingyu Fu, James Y. Huang, Zekun Li, Qin Liu, Xiaogeng Liu, Mingyu Derek Ma, Nan Xu, Wenxuan Zhou, Kai Zhang, Tianyi Lorena Yan, Wenjie Jacky Mo, Hsiang-Hui Liu, Pan Lu, Chunyuan Li, Chaowei Xiao, Kai-Wei Chang, Dan Roth, Sheng Zhang, Hoifung Poon, Muhao Chen·June 13, 2024

Summary

MUIRBENCH is a comprehensive benchmark for evaluating multi-image understanding in large language models, featuring 11,264 images and 2,600 questions across 12 diverse tasks. It tests models' ability to handle various multi-image relations, with a focus on challenging tasks like scene understanding and ordering. GPT-4 and Gemini Pro struggle, achieving 68.0% and 49.3% accuracy, while open-source single-image models fare poorly. MUIRBENCH differentiates from prior work by emphasizing multi-image reasoning and suggests avenues for future research in developing more robust multimodal LLMs. The benchmark assesses models' performance on tasks like visual retrieval, scene comprehension, and attribute similarity, and highlights the need for improved multi-image comprehension. The dataset is available under the Apache-2.0 license and is designed for academic use, with a focus on promoting research in this area.

Key findings

15

Paper digest

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

The paper "MuirBench: A Comprehensive Benchmark for Robust Multi-image Understanding" aims to address the challenge of evaluating multimodal large language models (LLMs) in understanding information conveyed through multiple images . This paper focuses on assessing the models' ability to comprehend and reason across various aspects of multi-image understanding, such as geographic understanding, diagram understanding, ordering images based on textual descriptions, visual grounding, visual retrieval, and more . The goal is to encourage the development of LLMs that can go beyond single-image tasks and excel in tasks requiring a holistic understanding of multiple images .

The problem tackled in the paper is not entirely new, as previous benchmarks have primarily focused on single-image questions, while MuirBench introduces a comprehensive range of 12 multi-image understanding abilities and evaluates models on 10 diverse multi-image relations . MuirBench also provides a robust evaluation by including unanswerable instance variants, which is a novel feature compared to prior benchmarks . The paper's emphasis on multi-image understanding and the creation of a benchmark specifically designed for evaluating models on multi-image tasks represent a significant contribution to the field of multimodal LLMs .


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate the hypothesis that even the best-performing models like GPT-4o and Gemini Pro find it challenging to solve the MUIRBENCH benchmark, achieving 68.0% and 49.3% accuracy, respectively. It also highlights that open-source multimodal LLMs trained on single images struggle to generalize to multi-image questions, achieving accuracy levels below 33.3% . The results emphasize the importance of developing multimodal LLMs that can extend beyond single-image understanding for future improvements .


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

The paper "MUIRBENCH: A Comprehensive Benchmark for Robust Multi-image Understanding" introduces several novel ideas, methods, and models in the field of multi-image understanding :

  • Comprehensive Benchmark: MUIRBENCH evaluates a wide range of 12 multi-image understanding abilities, covering tasks like geographic understanding and diagram understanding, which surpass previous benchmarks that mainly focus on single-image questions .
  • Multi-Image Relations: The benchmark includes 10 diverse multi-image relations such as narrative and complementary relations, providing a robust evaluation on models by incorporating unanswerable instance variants with minimal semantic differences .
  • Data Collection: The paper emphasizes the importance of collecting both answerable and unanswerable data instances to assess models' capabilities accurately. Strategies like image replacing or reordering, question modification, and option modification are employed to create unanswerable instances with minimal changes .
  • Metadata Annotation: Fine-grained metadata annotation is utilized to analyze multimodal LLMs' weaknesses across various aspects. Attributes like image relations, tasks, image types, number of images, and image positions are annotated to enhance diagnostic evaluation .
  • Quality Control: The paper employs automatic checks and manual examination to ensure data quality during the annotation process, resulting in the retention of 86.3% of instances. This quality control process enhances the reliability of the benchmark .
  • Multi-Image Relations Categories: MUIRBENCH consists of 10 multi-image relations, including temporal relations, ordered pages, narrative images, and more. These categories enhance the diversity and complexity of the benchmark, providing a comprehensive evaluation platform for multi-image understanding .
  • Pairwise Instance Creation: Each standard instance in MUIRBENCH is paired with an unanswerable variant with minimal semantic differences. This approach ensures a reliable assessment of models' capabilities in recognizing what they do not know, simulating real-world scenarios where queries may be unanswerable . The paper "MUIRBENCH: A Comprehensive Benchmark for Robust Multi-image Understanding" introduces several key characteristics and advantages compared to previous methods:
  • Comprehensive Evaluation: MUIRBENCH evaluates a wide range of 12 multi-image understanding abilities, such as geographic understanding and diagram understanding, surpassing previous benchmarks that mainly focus on single-image questions .
  • Diverse Multi-Image Relations: It includes 10 diverse multi-image relations like narrative and complementary relations, providing a robust evaluation on models by incorporating unanswerable instance variants with minimal semantic differences .
  • Unanswerable Data Collection: The benchmark employs strategies like image replacing or reordering, question modification, and option modification to create unanswerable instances with minimal changes, doubling the data size and leading to a balanced distribution of answerable and unanswerable instances .
  • Metadata Annotation: Fine-grained metadata annotation is utilized to analyze multimodal LLMs' weaknesses across various aspects, enhancing diagnostic evaluation. Attributes like image relations, tasks, image types, number of images, and image positions are annotated .
  • Quality Control: The paper employs automatic checks and manual examination to ensure data quality during the annotation process, resulting in the retention of 86.3% of instances. This quality control process enhances the reliability of the benchmark .
  • Multi-Image Relations Categories: MUIRBENCH consists of 10 multi-image relations, including temporal relations, ordered pages, narrative images, and more, enhancing the diversity and complexity of the benchmark for multi-image understanding .

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 researches exist in the field of robust multi-image understanding. Noteworthy researchers in this field include Fei Wang, Xingyu Fu, James Y. Huang, Zekun Li, Qin Liu, Xiaogeng Liu, Mingyu Derek Ma, Nan Xu, Wenxuan Zhou, Kai Zhang, Tianyi Lorena Yan, Wenjie Jacky Mo, Hsiang-Hui Liu, Pan Lu, Chunyuan Li, Chaowei Xiao, Kai-Wei Chang, Dan Roth, Sheng Zhang, Hoifung Poon, Muhao Chen, and many others . These researchers have contributed to the development of benchmarks and models for multi-image understanding.

The key to the solution mentioned in the paper "MUIRBENCH: A Comprehensive Benchmark for Robust Multi-image Understanding" is the creation of a benchmark that focuses on robust multi-image understanding capabilities of multimodal LLMs. MUIRBENCH consists of 12 diverse multi-image tasks involving 10 categories of multi-image relations, comprising 11,264 images and 2,600 multiple-choice questions. The benchmark is designed to provide a reliable assessment by pairing each standard instance with an unanswerable variant with minimal semantic differences. This approach ensures a thorough evaluation of multimodal LLMs in handling multi-image scenarios .


How were the experiments in the paper designed?

The experiments in the paper were designed by first describing the experimental setup and baselines, followed by a comprehensive evaluation of 20 recent multimodal LLMs . The evaluation included models designed for both multi-image inputs and single-image inputs, such as GPT-4o, GPT-4-Turbo, Gemini Pro, Mantis, VILA, Idefics, Emu2, OpenFlamingo, LLaVA, Yi-VL-6B4, MiniGPT-4-v2, and CogVLM . The study demonstrated that while humans could answer questions with high accuracy, the MUIRBENCH benchmark posed a challenge for existing models, with even the best-performing models like GPT-4o and Gemini Pro finding it difficult to solve MUIRBENCH .


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

The dataset used for quantitative evaluation in the study is MUIRBENCH, which is a comprehensive benchmark designed for robust multi-image understanding . The code for MUIRBENCH is open source and can be accessed on the Huggingface/Datasets platform, where the license and metadata are also available .


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 need to be verified. The paper introduces MUIRBENCH, a comprehensive benchmark designed to evaluate the multi-image understanding capabilities of multimodal LLMs . The benchmark consists of 12 diverse multi-image tasks involving various multi-image relations, providing a robust evaluation on these tasks . The results of the experiments conducted on 20 recent multi-modal LLMs, including well-known models like GPT-4 and Gemini Pro, reveal significant limitations in their ability to handle multi-image scenarios . These models struggled more with unanswerable questions in MUIRBENCH, indicating the need for improvement in their visual comprehension abilities .

The findings from the experiments underscore the importance of MUIRBENCH in encouraging the development of multimodal LLMs that can effectively synthesize and reason across multiple visual sources . The results highlight the challenges faced by even the best-performing models in solving the tasks presented in MUIRBENCH, emphasizing the need for advancements in multi-image understanding capabilities . The paper's analysis of model behavior under realistic settings and the pairwise design of the benchmark contribute to the reliability of the evaluation . Overall, the experiments and results provide valuable insights into the limitations of current multimodal LLMs and the potential pathways for future improvements in this field .


What are the contributions of this paper?

The paper "MUIRBENCH: A Comprehensive Benchmark for Robust Multi-image Understanding" makes several contributions:

  • It introduces MUIRBENCH, a benchmark focusing on robust multi-image understanding capabilities of multimodal LLMs, consisting of 12 diverse multi-image tasks involving 10 categories of multi-image relations .
  • MUIRBENCH contains 11,264 images and 2,600 multiple-choice questions, providing a reliable assessment by pairing each standard instance with an unanswerable variant with minimal semantic differences .
  • The benchmark evaluates recent multi-modal LLMs, revealing the challenges even the best-performing models face in solving MUIRBENCH, with models like GPT-4o and Gemini Pro achieving 68.0% and 49.3% accuracy, respectively .
  • The results emphasize the importance of MUIRBENCH in encouraging the development of multimodal LLMs that can go beyond single-image understanding, suggesting pathways for future improvements in this area .

What work can be continued in depth?

Further research in the field of multimodal large language models (LLMs) can focus on several areas to deepen the understanding and capabilities of these models:

  • Safety Issues in Multimodal Contexts: Encouraging more researchers to delve into safety issues related to multimodal LLMs can lead to the development of safer models that avoid generating harmful vision and text artifacts .
  • Development of Multimodal LLMs: There is a need to continue developing multimodal LLMs that can go beyond single-image understanding and tackle more complex tasks involving multiple images. This includes exploring pathways for future improvements in these models .
  • Fine-Tuning and Training Techniques: Research on efficient fine-tuning techniques for LLMs, such as the toolkit for fine-tuning LLMs, can contribute to enhancing the performance and capabilities of these models .
  • Benchmarking and Evaluation: Conducting rigorous benchmarking and evaluation of multimodal LLMs, like the MUIRBENCH benchmark, can provide insights into the strengths and limitations of these models in multi-image understanding tasks. This can help in identifying areas for improvement and innovation .

Introduction
Background
Overview of multi-image understanding in LLMs
Importance of multi-image reasoning in real-world scenarios
Objective
To evaluate LLMs' performance on multi-image tasks
Identify gaps and challenges for current models
Encourage research on robust multimodal LLM development
Method
Data Collection
Size and composition: 11,264 images, 2,600 questions, 12 diverse tasks
Source and diversity: Scenes, ordering, and various relations
Data Preprocessing
Image and question formatting
Annotation process for multi-image tasks
Task categorization: Visual retrieval, scene comprehension, attribute similarity
Benchmark Tasks
Visual Retrieval
Task description
Performance comparison (GPT-4, Gemini Pro, open-source models)
Scene Comprehension
Scene understanding tasks
Model performance analysis
Ordering and Sequencing
Assessing models' ability to arrange images chronologically or logically
Attribute Similarity
Evaluating models on matching image attributes
Challenging Tasks
Scene understanding and reasoning examples
GPT-4 and Gemini Pro struggles
Results and Analysis
Accuracy scores for different models
Strengths and weaknesses of current LLMs
Comparative analysis with prior benchmarks
Future Research Directions
Opportunities for model improvement
Multimodal LLM development strategies
Limitations and potential advancements
Conclusion
MUIRBENCH's contribution to the field
Importance of the benchmark for promoting research
Availability and licensing (Apache-2.0)
Acknowledgments
Dataset creators and contributors
Licensing and usage guidelines for academic community
Basic info
papers
computer vision and pattern recognition
computation and language
artificial intelligence
Advanced features
Insights
What are some key tasks and challenges tested in MUIRBENCH?
How many images and questions does MUIRBENCH contain?
Which models have the lowest accuracy in the MUIRBENCH benchmark?
What is MUIRBENCH used for?

MuirBench: A Comprehensive Benchmark for Robust Multi-image Understanding

Fei Wang, Xingyu Fu, James Y. Huang, Zekun Li, Qin Liu, Xiaogeng Liu, Mingyu Derek Ma, Nan Xu, Wenxuan Zhou, Kai Zhang, Tianyi Lorena Yan, Wenjie Jacky Mo, Hsiang-Hui Liu, Pan Lu, Chunyuan Li, Chaowei Xiao, Kai-Wei Chang, Dan Roth, Sheng Zhang, Hoifung Poon, Muhao Chen·June 13, 2024

Summary

MUIRBENCH is a comprehensive benchmark for evaluating multi-image understanding in large language models, featuring 11,264 images and 2,600 questions across 12 diverse tasks. It tests models' ability to handle various multi-image relations, with a focus on challenging tasks like scene understanding and ordering. GPT-4 and Gemini Pro struggle, achieving 68.0% and 49.3% accuracy, while open-source single-image models fare poorly. MUIRBENCH differentiates from prior work by emphasizing multi-image reasoning and suggests avenues for future research in developing more robust multimodal LLMs. The benchmark assesses models' performance on tasks like visual retrieval, scene comprehension, and attribute similarity, and highlights the need for improved multi-image comprehension. The dataset is available under the Apache-2.0 license and is designed for academic use, with a focus on promoting research in this area.
Mind map
GPT-4 and Gemini Pro struggles
Scene understanding and reasoning examples
Model performance analysis
Scene understanding tasks
Performance comparison (GPT-4, Gemini Pro, open-source models)
Task description
Task categorization: Visual retrieval, scene comprehension, attribute similarity
Annotation process for multi-image tasks
Image and question formatting
Source and diversity: Scenes, ordering, and various relations
Size and composition: 11,264 images, 2,600 questions, 12 diverse tasks
Encourage research on robust multimodal LLM development
Identify gaps and challenges for current models
To evaluate LLMs' performance on multi-image tasks
Importance of multi-image reasoning in real-world scenarios
Overview of multi-image understanding in LLMs
Licensing and usage guidelines for academic community
Dataset creators and contributors
Availability and licensing (Apache-2.0)
Importance of the benchmark for promoting research
MUIRBENCH's contribution to the field
Limitations and potential advancements
Multimodal LLM development strategies
Opportunities for model improvement
Comparative analysis with prior benchmarks
Strengths and weaknesses of current LLMs
Accuracy scores for different models
Challenging Tasks
Evaluating models on matching image attributes
Attribute Similarity
Assessing models' ability to arrange images chronologically or logically
Ordering and Sequencing
Scene Comprehension
Visual Retrieval
Data Preprocessing
Data Collection
Objective
Background
Acknowledgments
Conclusion
Future Research Directions
Results and Analysis
Benchmark Tasks
Method
Introduction
Outline
Introduction
Background
Overview of multi-image understanding in LLMs
Importance of multi-image reasoning in real-world scenarios
Objective
To evaluate LLMs' performance on multi-image tasks
Identify gaps and challenges for current models
Encourage research on robust multimodal LLM development
Method
Data Collection
Size and composition: 11,264 images, 2,600 questions, 12 diverse tasks
Source and diversity: Scenes, ordering, and various relations
Data Preprocessing
Image and question formatting
Annotation process for multi-image tasks
Task categorization: Visual retrieval, scene comprehension, attribute similarity
Benchmark Tasks
Visual Retrieval
Task description
Performance comparison (GPT-4, Gemini Pro, open-source models)
Scene Comprehension
Scene understanding tasks
Model performance analysis
Ordering and Sequencing
Assessing models' ability to arrange images chronologically or logically
Attribute Similarity
Evaluating models on matching image attributes
Challenging Tasks
Scene understanding and reasoning examples
GPT-4 and Gemini Pro struggles
Results and Analysis
Accuracy scores for different models
Strengths and weaknesses of current LLMs
Comparative analysis with prior benchmarks
Future Research Directions
Opportunities for model improvement
Multimodal LLM development strategies
Limitations and potential advancements
Conclusion
MUIRBENCH's contribution to the field
Importance of the benchmark for promoting research
Availability and licensing (Apache-2.0)
Acknowledgments
Dataset creators and contributors
Licensing and usage guidelines for academic community
Key findings
15

Paper digest

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

The paper "MuirBench: A Comprehensive Benchmark for Robust Multi-image Understanding" aims to address the challenge of evaluating multimodal large language models (LLMs) in understanding information conveyed through multiple images . This paper focuses on assessing the models' ability to comprehend and reason across various aspects of multi-image understanding, such as geographic understanding, diagram understanding, ordering images based on textual descriptions, visual grounding, visual retrieval, and more . The goal is to encourage the development of LLMs that can go beyond single-image tasks and excel in tasks requiring a holistic understanding of multiple images .

The problem tackled in the paper is not entirely new, as previous benchmarks have primarily focused on single-image questions, while MuirBench introduces a comprehensive range of 12 multi-image understanding abilities and evaluates models on 10 diverse multi-image relations . MuirBench also provides a robust evaluation by including unanswerable instance variants, which is a novel feature compared to prior benchmarks . The paper's emphasis on multi-image understanding and the creation of a benchmark specifically designed for evaluating models on multi-image tasks represent a significant contribution to the field of multimodal LLMs .


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate the hypothesis that even the best-performing models like GPT-4o and Gemini Pro find it challenging to solve the MUIRBENCH benchmark, achieving 68.0% and 49.3% accuracy, respectively. It also highlights that open-source multimodal LLMs trained on single images struggle to generalize to multi-image questions, achieving accuracy levels below 33.3% . The results emphasize the importance of developing multimodal LLMs that can extend beyond single-image understanding for future improvements .


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

The paper "MUIRBENCH: A Comprehensive Benchmark for Robust Multi-image Understanding" introduces several novel ideas, methods, and models in the field of multi-image understanding :

  • Comprehensive Benchmark: MUIRBENCH evaluates a wide range of 12 multi-image understanding abilities, covering tasks like geographic understanding and diagram understanding, which surpass previous benchmarks that mainly focus on single-image questions .
  • Multi-Image Relations: The benchmark includes 10 diverse multi-image relations such as narrative and complementary relations, providing a robust evaluation on models by incorporating unanswerable instance variants with minimal semantic differences .
  • Data Collection: The paper emphasizes the importance of collecting both answerable and unanswerable data instances to assess models' capabilities accurately. Strategies like image replacing or reordering, question modification, and option modification are employed to create unanswerable instances with minimal changes .
  • Metadata Annotation: Fine-grained metadata annotation is utilized to analyze multimodal LLMs' weaknesses across various aspects. Attributes like image relations, tasks, image types, number of images, and image positions are annotated to enhance diagnostic evaluation .
  • Quality Control: The paper employs automatic checks and manual examination to ensure data quality during the annotation process, resulting in the retention of 86.3% of instances. This quality control process enhances the reliability of the benchmark .
  • Multi-Image Relations Categories: MUIRBENCH consists of 10 multi-image relations, including temporal relations, ordered pages, narrative images, and more. These categories enhance the diversity and complexity of the benchmark, providing a comprehensive evaluation platform for multi-image understanding .
  • Pairwise Instance Creation: Each standard instance in MUIRBENCH is paired with an unanswerable variant with minimal semantic differences. This approach ensures a reliable assessment of models' capabilities in recognizing what they do not know, simulating real-world scenarios where queries may be unanswerable . The paper "MUIRBENCH: A Comprehensive Benchmark for Robust Multi-image Understanding" introduces several key characteristics and advantages compared to previous methods:
  • Comprehensive Evaluation: MUIRBENCH evaluates a wide range of 12 multi-image understanding abilities, such as geographic understanding and diagram understanding, surpassing previous benchmarks that mainly focus on single-image questions .
  • Diverse Multi-Image Relations: It includes 10 diverse multi-image relations like narrative and complementary relations, providing a robust evaluation on models by incorporating unanswerable instance variants with minimal semantic differences .
  • Unanswerable Data Collection: The benchmark employs strategies like image replacing or reordering, question modification, and option modification to create unanswerable instances with minimal changes, doubling the data size and leading to a balanced distribution of answerable and unanswerable instances .
  • Metadata Annotation: Fine-grained metadata annotation is utilized to analyze multimodal LLMs' weaknesses across various aspects, enhancing diagnostic evaluation. Attributes like image relations, tasks, image types, number of images, and image positions are annotated .
  • Quality Control: The paper employs automatic checks and manual examination to ensure data quality during the annotation process, resulting in the retention of 86.3% of instances. This quality control process enhances the reliability of the benchmark .
  • Multi-Image Relations Categories: MUIRBENCH consists of 10 multi-image relations, including temporal relations, ordered pages, narrative images, and more, enhancing the diversity and complexity of the benchmark for multi-image understanding .

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 researches exist in the field of robust multi-image understanding. Noteworthy researchers in this field include Fei Wang, Xingyu Fu, James Y. Huang, Zekun Li, Qin Liu, Xiaogeng Liu, Mingyu Derek Ma, Nan Xu, Wenxuan Zhou, Kai Zhang, Tianyi Lorena Yan, Wenjie Jacky Mo, Hsiang-Hui Liu, Pan Lu, Chunyuan Li, Chaowei Xiao, Kai-Wei Chang, Dan Roth, Sheng Zhang, Hoifung Poon, Muhao Chen, and many others . These researchers have contributed to the development of benchmarks and models for multi-image understanding.

The key to the solution mentioned in the paper "MUIRBENCH: A Comprehensive Benchmark for Robust Multi-image Understanding" is the creation of a benchmark that focuses on robust multi-image understanding capabilities of multimodal LLMs. MUIRBENCH consists of 12 diverse multi-image tasks involving 10 categories of multi-image relations, comprising 11,264 images and 2,600 multiple-choice questions. The benchmark is designed to provide a reliable assessment by pairing each standard instance with an unanswerable variant with minimal semantic differences. This approach ensures a thorough evaluation of multimodal LLMs in handling multi-image scenarios .


How were the experiments in the paper designed?

The experiments in the paper were designed by first describing the experimental setup and baselines, followed by a comprehensive evaluation of 20 recent multimodal LLMs . The evaluation included models designed for both multi-image inputs and single-image inputs, such as GPT-4o, GPT-4-Turbo, Gemini Pro, Mantis, VILA, Idefics, Emu2, OpenFlamingo, LLaVA, Yi-VL-6B4, MiniGPT-4-v2, and CogVLM . The study demonstrated that while humans could answer questions with high accuracy, the MUIRBENCH benchmark posed a challenge for existing models, with even the best-performing models like GPT-4o and Gemini Pro finding it difficult to solve MUIRBENCH .


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

The dataset used for quantitative evaluation in the study is MUIRBENCH, which is a comprehensive benchmark designed for robust multi-image understanding . The code for MUIRBENCH is open source and can be accessed on the Huggingface/Datasets platform, where the license and metadata are also available .


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 need to be verified. The paper introduces MUIRBENCH, a comprehensive benchmark designed to evaluate the multi-image understanding capabilities of multimodal LLMs . The benchmark consists of 12 diverse multi-image tasks involving various multi-image relations, providing a robust evaluation on these tasks . The results of the experiments conducted on 20 recent multi-modal LLMs, including well-known models like GPT-4 and Gemini Pro, reveal significant limitations in their ability to handle multi-image scenarios . These models struggled more with unanswerable questions in MUIRBENCH, indicating the need for improvement in their visual comprehension abilities .

The findings from the experiments underscore the importance of MUIRBENCH in encouraging the development of multimodal LLMs that can effectively synthesize and reason across multiple visual sources . The results highlight the challenges faced by even the best-performing models in solving the tasks presented in MUIRBENCH, emphasizing the need for advancements in multi-image understanding capabilities . The paper's analysis of model behavior under realistic settings and the pairwise design of the benchmark contribute to the reliability of the evaluation . Overall, the experiments and results provide valuable insights into the limitations of current multimodal LLMs and the potential pathways for future improvements in this field .


What are the contributions of this paper?

The paper "MUIRBENCH: A Comprehensive Benchmark for Robust Multi-image Understanding" makes several contributions:

  • It introduces MUIRBENCH, a benchmark focusing on robust multi-image understanding capabilities of multimodal LLMs, consisting of 12 diverse multi-image tasks involving 10 categories of multi-image relations .
  • MUIRBENCH contains 11,264 images and 2,600 multiple-choice questions, providing a reliable assessment by pairing each standard instance with an unanswerable variant with minimal semantic differences .
  • The benchmark evaluates recent multi-modal LLMs, revealing the challenges even the best-performing models face in solving MUIRBENCH, with models like GPT-4o and Gemini Pro achieving 68.0% and 49.3% accuracy, respectively .
  • The results emphasize the importance of MUIRBENCH in encouraging the development of multimodal LLMs that can go beyond single-image understanding, suggesting pathways for future improvements in this area .

What work can be continued in depth?

Further research in the field of multimodal large language models (LLMs) can focus on several areas to deepen the understanding and capabilities of these models:

  • Safety Issues in Multimodal Contexts: Encouraging more researchers to delve into safety issues related to multimodal LLMs can lead to the development of safer models that avoid generating harmful vision and text artifacts .
  • Development of Multimodal LLMs: There is a need to continue developing multimodal LLMs that can go beyond single-image understanding and tackle more complex tasks involving multiple images. This includes exploring pathways for future improvements in these models .
  • Fine-Tuning and Training Techniques: Research on efficient fine-tuning techniques for LLMs, such as the toolkit for fine-tuning LLMs, can contribute to enhancing the performance and capabilities of these models .
  • Benchmarking and Evaluation: Conducting rigorous benchmarking and evaluation of multimodal LLMs, like the MUIRBENCH benchmark, can provide insights into the strengths and limitations of these models in multi-image understanding tasks. This can help in identifying areas for improvement and innovation .
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