Synthetic Multimodal Question Generation

Ian Wu, Sravan Jayanthi, Vijay Viswanathan, Simon Rosenberg, Sina Pakazad, Tongshuang Wu, Graham Neubig·July 02, 2024

Summary

The paper introduces SMMQG, a synthetic data generation framework for multimodal retrieval and question answering (MMRAG) that creates diverse, style-specific datasets using a retriever, LLM, and LMM. It addresses the lack of high-quality evaluation datasets by generating a 1024-question Wikipedia-based dataset and comparing it to MMQA. Human studies confirm SMMQG's data quality, with downstream evaluations revealing insights into model performance. The study evaluates various models, including open-source and proprietary ones, and highlights the importance of controlling question styles and modalities for accurate assessment. SMMQG offers a valuable tool for fine-grained evaluation of multimodal reasoning systems.

Key findings

12

Paper digest

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

The paper aims to address the task of Synthetic Multimodal Question Generation (SMMQG) by evaluating different methods for creating questions and answers for reading comprehension tasks across various question styles and modalities . This problem involves generating questions and answers connected to different sources of information, such as text, tables, or images, and assessing their quality based on specific criteria . The study explores the performance of different models and techniques in generating questions and answers, focusing on five question styles and all pairwise modality combinations . While the task of SMMQG itself is not entirely new, the paper contributes by evaluating the effectiveness of various models and methods in generating questions and answers across different modalities and question styles, providing insights into the performance of different approaches in this domain .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the effectiveness of a Synthetic Multimodal Question Generation (SMMQG) framework for generating multimodal questions and answers based on input documents, with fine-grained control over question styles and modalities, including unimodal and cross-modal questions . The study focuses on creating a dataset of 1024 questions and answers of various styles and modalities from Wikipedia documents to evaluate the performance of different models in multimodal question-answering tasks .


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

The paper "Synthetic Multimodal Question Generation" proposes several new ideas, methods, and models in the field of question generation and reading comprehension tasks . Some key contributions include:

  • Introducing different question styles such as Information Extraction, Compare Contrast, Numerical, Compound, and Multi-hop .
  • Evaluating various methods for creating questions and answers for reading comprehension tasks, involving crowdworkers to assess the quality of questions and answers based on specific criteria .
  • Presenting retrieval and QA evaluation results by question style, showcasing the performance of different models like E5, Claude 3 Opus, and GPT-4-Turbo across various metrics .
  • Introducing proprietary multimodal models like Gemini Pro 1.0, the Claude 3 family, and GPT-4-Turbo, each with its unique characteristics and performance .
  • Providing detailed instructions and rules for generating questions based on images, text, and tables, ensuring the questions are fluent, faithful to the question style, and relevant to the provided sources .
  • Offering assessment criteria for question fluency, faithfulness to the question style, source relevance, answerability, and answer correctness, guiding the evaluation process of generated questions and answers . The paper "Synthetic Multimodal Question Generation" introduces several novel characteristics and advantages compared to previous methods in the field of question generation and reading comprehension tasks. Here are some key points based on the details in the paper:
  1. Question Styles and Modalities: The paper presents a diverse set of question styles, including Information Extraction, Compare Contrast, Numerical, Compound, and Multi-hop, each requiring different types of reasoning and information extraction from various modalities such as text, tables, and images .

  2. Evaluation Metrics: The paper evaluates the performance of different models using metrics like recall@5 and recall@10 for retrieval tasks and GPT-4-Turbo judge scores for question answering tasks. It compares the performance of open-source models like Vicuna-7b, Qwen-Chat, and proprietary multimodal models like Gemini Pro 1.0, Claude 3 family, and GPT-4-Turbo, showcasing the superiority of proprietary models in certain aspects .

  3. Model Performance: The proprietary multimodal models, such as Gemini Pro 1.0, Claude 3 Opus, and GPT-4-Turbo, demonstrate high performance across different question styles, with GPT-4-Turbo achieving the highest scores in terms of retrieval and question answering tasks. These models outperform open-source models in various evaluation metrics, highlighting their effectiveness in generating questions and answers .

  4. Dataset Creation: The paper introduces a Synthetic Multimodal Question Generation (SMMQG) dataset consisting of 1024 QA pairs across different question styles and modalities. This dataset is designed to test diverse reasoning abilities and is based on Wikipedia documents, providing a rich source for generating questions and evaluating models .

  5. Innovative Question Generation: The paper proposes innovative methods for generating questions, including multi-hop questions that require resolving implicit sub-questions to derive the final answer. By combining intermediate questions and answers, the model produces high-quality multi-hop questions consistently, enhancing the complexity and quality of generated questions .

Overall, the paper's contributions lie in its introduction of diverse question styles, thorough evaluation of models, comparison of open-source and proprietary models, dataset creation for multimodal question generation, and innovative approaches to generating complex questions, showcasing advancements in the field of question generation and reading comprehension 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 researches exist in the field of Synthetic Multimodal Question Generation. Noteworthy researchers in this field include Zheng et al., Kim et al., Liu et al., Bai et al., Peng et al., and Anthropic . The key to the solution mentioned in the paper involves using a Synthetic Multimodal Question Generation framework (SMMQG) to generate multimodal questions and answers based directly on input documents, enabling fine-grained control over question styles and modalities, and producing both unimodal and cross-modal questions .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the performance of three retrievers and eight LLM + LMM combinations . The retrieval evaluation included models like BM25, E5-Large, and OpenCLIP, while the QA evaluation involved models such as Vicuna, Qwen-Chat, Gemini Pro, and the Claude 3 family, among others . The experiments aimed to assess the performance of these models across different question styles and modalities using a Synthetic Multimodal Question Generation (SMMQG) framework .


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

The dataset used for quantitative evaluation in the study is the SMMQG dataset . The code for the open-source models used in the experiments, such as E5-Large, OpenCLIP, LLaVA-13b, LLaVA-v1.5-7b, LLaVA-v1.5-13b, Vicuna-7b-v1.5, Vicuna-13b-v1.5, Qwen-Chat, Qwen-Chat-VL, GPT-4-Turbo, Gemini Pro 1.0, Claude 3 Haiku, and Claude 3, is open source .


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 Synthetic Multimodal Question Generation (SMMQG) framework introduced in the study leverages a retriever, a Large Language Model (LLM), and a Large Multimodal Model (LMM) to generate multimodal questions and answers based on input documents . The evaluation of various model combinations, including open-source and proprietary models, demonstrates the effectiveness of the SMMQG approach in generating questions across different styles and modalities . The results show that the GPT-4-Turbo model achieved high scores in question verification, indicating the quality and accuracy of the generated questions and answers . Additionally, the study conducted a human study to measure the dataset's quality, which was found to be on par with or better than popular crowdsourced benchmark datasets . These findings validate the efficacy of the SMMQG framework in generating synthetic data for multimodal question answering tasks and highlight its potential for model selection and evaluation in the field of multimodal question generation and answering .


What are the contributions of this paper?

The contributions of this paper include:

  • Building a Synthetic Multimodal Wikipedia QA Dataset using SMMQG, consisting of 1024 QA pairs across five question styles and all pairwise modality combinations .
  • Evaluating the performance of three retrievers and eight LLM + LMM combinations using the dataset, assessing retrieval with BM25, E5-Large, and OpenCLIP, and evaluating QA models .

What work can be continued in depth?

To delve deeper into the work outlined in the document, further exploration can be conducted on the generation of multi-hop questions. This involves generating two intermediate questions and their answers based on extracted entities, then combining them to form a multi-hop question and answer. The process includes splitting information by modality and unifying question sources chosen in the intermediate steps . Additionally, the evaluation of retrievers and QA models, such as BM25, E5, OpenCLIP, and various multimodal models like GPT-4-Turbo, Gemini Pro 1.0, and Claude 3 family, can be further analyzed for performance across different question styles .

Tables

5

Introduction
Background
Limited high-quality MMRQ datasets
Importance of diverse and style-specific data
Objective
To address data scarcity in MMRQ evaluation
Develop a framework for generating synthetic datasets
Human study validation and downstream evaluation
Method
Data Collection
Retrieval-based Approach
Wikipedia as the source corpus
Query generation using a retriever model
LLM and LMM Integration
Large Language Models (LLM) for question and answer synthesis
Language Models for multimodal content generation
Data Preprocessing
Style control and diversity
Ensuring factual correctness
Filtering and validation process
Dataset Generation
SMMQG Process
Retrieval of relevant Wikipedia articles
Query formulation with LLM
Answer and context generation with LMM
Style customization and variation
Human Evaluation
Study Design
Sample dataset comparison with MMQA
Assessing data quality, relevance, and diversity
Results and Feedback
Human perception of synthetic data quality
Importance of style and modality control
Model Evaluation
Downstream Tasks
Multimodal retrieval and question answering
Comparison of open-source and proprietary models
Insights and Performance Analysis
Model strengths and weaknesses
Fine-grained evaluation implications
Conclusion
SMMQG's contribution to MMRQ benchmarking
Practical implications for model development
Future directions and potential improvements
Basic info
papers
computation and language
machine learning
artificial intelligence
Advanced features
Insights
What insights does the human study and downstream evaluations provide about model performance using SMMQG?
What problem does SMMQG aim to address in the context of multimodal retrieval and question answering?
How does SMMQG generate synthetic data, and what is the significance of its Wikipedia-based dataset?
What is the primary focus of the SMMQG framework?

Synthetic Multimodal Question Generation

Ian Wu, Sravan Jayanthi, Vijay Viswanathan, Simon Rosenberg, Sina Pakazad, Tongshuang Wu, Graham Neubig·July 02, 2024

Summary

The paper introduces SMMQG, a synthetic data generation framework for multimodal retrieval and question answering (MMRAG) that creates diverse, style-specific datasets using a retriever, LLM, and LMM. It addresses the lack of high-quality evaluation datasets by generating a 1024-question Wikipedia-based dataset and comparing it to MMQA. Human studies confirm SMMQG's data quality, with downstream evaluations revealing insights into model performance. The study evaluates various models, including open-source and proprietary ones, and highlights the importance of controlling question styles and modalities for accurate assessment. SMMQG offers a valuable tool for fine-grained evaluation of multimodal reasoning systems.
Mind map
Language Models for multimodal content generation
Large Language Models (LLM) for question and answer synthesis
Query generation using a retriever model
Wikipedia as the source corpus
Fine-grained evaluation implications
Model strengths and weaknesses
Comparison of open-source and proprietary models
Multimodal retrieval and question answering
Importance of style and modality control
Human perception of synthetic data quality
Assessing data quality, relevance, and diversity
Sample dataset comparison with MMQA
Style customization and variation
Answer and context generation with LMM
Query formulation with LLM
Retrieval of relevant Wikipedia articles
Filtering and validation process
Ensuring factual correctness
Style control and diversity
LLM and LMM Integration
Retrieval-based Approach
Human study validation and downstream evaluation
Develop a framework for generating synthetic datasets
To address data scarcity in MMRQ evaluation
Importance of diverse and style-specific data
Limited high-quality MMRQ datasets
Future directions and potential improvements
Practical implications for model development
SMMQG's contribution to MMRQ benchmarking
Insights and Performance Analysis
Downstream Tasks
Results and Feedback
Study Design
SMMQG Process
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Model Evaluation
Human Evaluation
Dataset Generation
Method
Introduction
Outline
Introduction
Background
Limited high-quality MMRQ datasets
Importance of diverse and style-specific data
Objective
To address data scarcity in MMRQ evaluation
Develop a framework for generating synthetic datasets
Human study validation and downstream evaluation
Method
Data Collection
Retrieval-based Approach
Wikipedia as the source corpus
Query generation using a retriever model
LLM and LMM Integration
Large Language Models (LLM) for question and answer synthesis
Language Models for multimodal content generation
Data Preprocessing
Style control and diversity
Ensuring factual correctness
Filtering and validation process
Dataset Generation
SMMQG Process
Retrieval of relevant Wikipedia articles
Query formulation with LLM
Answer and context generation with LMM
Style customization and variation
Human Evaluation
Study Design
Sample dataset comparison with MMQA
Assessing data quality, relevance, and diversity
Results and Feedback
Human perception of synthetic data quality
Importance of style and modality control
Model Evaluation
Downstream Tasks
Multimodal retrieval and question answering
Comparison of open-source and proprietary models
Insights and Performance Analysis
Model strengths and weaknesses
Fine-grained evaluation implications
Conclusion
SMMQG's contribution to MMRQ benchmarking
Practical implications for model development
Future directions and potential improvements
Key findings
12

Paper digest

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

The paper aims to address the task of Synthetic Multimodal Question Generation (SMMQG) by evaluating different methods for creating questions and answers for reading comprehension tasks across various question styles and modalities . This problem involves generating questions and answers connected to different sources of information, such as text, tables, or images, and assessing their quality based on specific criteria . The study explores the performance of different models and techniques in generating questions and answers, focusing on five question styles and all pairwise modality combinations . While the task of SMMQG itself is not entirely new, the paper contributes by evaluating the effectiveness of various models and methods in generating questions and answers across different modalities and question styles, providing insights into the performance of different approaches in this domain .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the effectiveness of a Synthetic Multimodal Question Generation (SMMQG) framework for generating multimodal questions and answers based on input documents, with fine-grained control over question styles and modalities, including unimodal and cross-modal questions . The study focuses on creating a dataset of 1024 questions and answers of various styles and modalities from Wikipedia documents to evaluate the performance of different models in multimodal question-answering tasks .


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

The paper "Synthetic Multimodal Question Generation" proposes several new ideas, methods, and models in the field of question generation and reading comprehension tasks . Some key contributions include:

  • Introducing different question styles such as Information Extraction, Compare Contrast, Numerical, Compound, and Multi-hop .
  • Evaluating various methods for creating questions and answers for reading comprehension tasks, involving crowdworkers to assess the quality of questions and answers based on specific criteria .
  • Presenting retrieval and QA evaluation results by question style, showcasing the performance of different models like E5, Claude 3 Opus, and GPT-4-Turbo across various metrics .
  • Introducing proprietary multimodal models like Gemini Pro 1.0, the Claude 3 family, and GPT-4-Turbo, each with its unique characteristics and performance .
  • Providing detailed instructions and rules for generating questions based on images, text, and tables, ensuring the questions are fluent, faithful to the question style, and relevant to the provided sources .
  • Offering assessment criteria for question fluency, faithfulness to the question style, source relevance, answerability, and answer correctness, guiding the evaluation process of generated questions and answers . The paper "Synthetic Multimodal Question Generation" introduces several novel characteristics and advantages compared to previous methods in the field of question generation and reading comprehension tasks. Here are some key points based on the details in the paper:
  1. Question Styles and Modalities: The paper presents a diverse set of question styles, including Information Extraction, Compare Contrast, Numerical, Compound, and Multi-hop, each requiring different types of reasoning and information extraction from various modalities such as text, tables, and images .

  2. Evaluation Metrics: The paper evaluates the performance of different models using metrics like recall@5 and recall@10 for retrieval tasks and GPT-4-Turbo judge scores for question answering tasks. It compares the performance of open-source models like Vicuna-7b, Qwen-Chat, and proprietary multimodal models like Gemini Pro 1.0, Claude 3 family, and GPT-4-Turbo, showcasing the superiority of proprietary models in certain aspects .

  3. Model Performance: The proprietary multimodal models, such as Gemini Pro 1.0, Claude 3 Opus, and GPT-4-Turbo, demonstrate high performance across different question styles, with GPT-4-Turbo achieving the highest scores in terms of retrieval and question answering tasks. These models outperform open-source models in various evaluation metrics, highlighting their effectiveness in generating questions and answers .

  4. Dataset Creation: The paper introduces a Synthetic Multimodal Question Generation (SMMQG) dataset consisting of 1024 QA pairs across different question styles and modalities. This dataset is designed to test diverse reasoning abilities and is based on Wikipedia documents, providing a rich source for generating questions and evaluating models .

  5. Innovative Question Generation: The paper proposes innovative methods for generating questions, including multi-hop questions that require resolving implicit sub-questions to derive the final answer. By combining intermediate questions and answers, the model produces high-quality multi-hop questions consistently, enhancing the complexity and quality of generated questions .

Overall, the paper's contributions lie in its introduction of diverse question styles, thorough evaluation of models, comparison of open-source and proprietary models, dataset creation for multimodal question generation, and innovative approaches to generating complex questions, showcasing advancements in the field of question generation and reading comprehension 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 researches exist in the field of Synthetic Multimodal Question Generation. Noteworthy researchers in this field include Zheng et al., Kim et al., Liu et al., Bai et al., Peng et al., and Anthropic . The key to the solution mentioned in the paper involves using a Synthetic Multimodal Question Generation framework (SMMQG) to generate multimodal questions and answers based directly on input documents, enabling fine-grained control over question styles and modalities, and producing both unimodal and cross-modal questions .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the performance of three retrievers and eight LLM + LMM combinations . The retrieval evaluation included models like BM25, E5-Large, and OpenCLIP, while the QA evaluation involved models such as Vicuna, Qwen-Chat, Gemini Pro, and the Claude 3 family, among others . The experiments aimed to assess the performance of these models across different question styles and modalities using a Synthetic Multimodal Question Generation (SMMQG) framework .


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

The dataset used for quantitative evaluation in the study is the SMMQG dataset . The code for the open-source models used in the experiments, such as E5-Large, OpenCLIP, LLaVA-13b, LLaVA-v1.5-7b, LLaVA-v1.5-13b, Vicuna-7b-v1.5, Vicuna-13b-v1.5, Qwen-Chat, Qwen-Chat-VL, GPT-4-Turbo, Gemini Pro 1.0, Claude 3 Haiku, and Claude 3, is open source .


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 Synthetic Multimodal Question Generation (SMMQG) framework introduced in the study leverages a retriever, a Large Language Model (LLM), and a Large Multimodal Model (LMM) to generate multimodal questions and answers based on input documents . The evaluation of various model combinations, including open-source and proprietary models, demonstrates the effectiveness of the SMMQG approach in generating questions across different styles and modalities . The results show that the GPT-4-Turbo model achieved high scores in question verification, indicating the quality and accuracy of the generated questions and answers . Additionally, the study conducted a human study to measure the dataset's quality, which was found to be on par with or better than popular crowdsourced benchmark datasets . These findings validate the efficacy of the SMMQG framework in generating synthetic data for multimodal question answering tasks and highlight its potential for model selection and evaluation in the field of multimodal question generation and answering .


What are the contributions of this paper?

The contributions of this paper include:

  • Building a Synthetic Multimodal Wikipedia QA Dataset using SMMQG, consisting of 1024 QA pairs across five question styles and all pairwise modality combinations .
  • Evaluating the performance of three retrievers and eight LLM + LMM combinations using the dataset, assessing retrieval with BM25, E5-Large, and OpenCLIP, and evaluating QA models .

What work can be continued in depth?

To delve deeper into the work outlined in the document, further exploration can be conducted on the generation of multi-hop questions. This involves generating two intermediate questions and their answers based on extracted entities, then combining them to form a multi-hop question and answer. The process includes splitting information by modality and unifying question sources chosen in the intermediate steps . Additionally, the evaluation of retrievers and QA models, such as BM25, E5, OpenCLIP, and various multimodal models like GPT-4-Turbo, Gemini Pro 1.0, and Claude 3 family, can be further analyzed for performance across different question styles .

Tables
5
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