mDPO: Conditional Preference Optimization for Multimodal Large Language Models

Fei Wang, Wenxuan Zhou, James Y. Huang, Nan Xu, Sheng Zhang, Hoifung Poon, Muhao Chen·June 17, 2024

Summary

The paper investigates the challenges in applying Direct Preference Optimization (DPO) to multimodal large language models, specifically highlighting an "unconditional preference problem" where models ignore image conditions. To address this, the authors propose MDPO (Multimodal DPO), which combines language and image preference optimization, introducing a reward anchor to ensure positive rewards. Experiments with Bunny-v1.0-3B and LLaVA-v1.5-7B models on three benchmarks (MMHalBench, AMBER, and a generative task) show that MDPO effectively resolves the issue, improving model performance, reducing hallucinations, and even matching or outperforming DPO in some cases. Human evaluations further confirm MDPO's superiority. Future work suggests expanding the evaluation to diverse models and exploring combinations with other preference enhancement methods. The overall focus is on enhancing multimodal model alignment and reducing hallucinations in AI systems.

Key findings

4

Paper digest

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

The paper aims to address the unconditional preference problem in multimodal preference optimization, specifically in the context of large language models (LLMs) . This problem arises when applying Direct Preference Optimization (DPO) to multimodal LLMs, where the model overlooks the image condition in the preference dataset, leading to inconsistent improvements in model capabilities . The paper introduces a variant called DPO (No Image) to explore this issue, highlighting the challenge of effectively utilizing the visual modality in preference data . This problem is not entirely new, as recent studies have identified similar issues but have attributed them to the quality of preference data rather than the model's handling of the visual modality .


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate the scientific hypothesis related to multimodal preference optimization, specifically addressing the unconditional preference problem in multimodal preference optimization . The study aims to explore and propose a solution, MDPO (Multimodal Conditional Preference Optimization), to prevent the over-prioritization of language-only preferences by optimizing image preference as well, thereby improving model performance and reducing hallucination in multimodal large language models . The research focuses on enhancing the alignment of large language models with human preferences in multimodal scenarios, building on the success of Direct Preference Optimization (DPO) in the language modality .


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

The paper proposes several novel ideas, methods, and models in the field of multimodal large language models (LLMs) optimization:

  • Conditional Preference Optimization (CoPO): The paper introduces CoPO as a method to address the challenge of preference optimization neglecting images in multimodal scenarios. CoPO combines standard DPO, conditional preference optimization, and anchored preference optimization to enhance multimodal DPO .
  • Anchored Preference Optimization (AncPO): The paper incorporates AncPO as part of the objective of MDPO, which involves setting an anchor for the reward to be lower than a specific value. This approach aims to improve the optimization process by introducing anchors for rewards .
  • Iterative DPO and Sampling Preference Data: The paper discusses Iterative DPO and SPPO, which propose sampling preference data in an on-policy manner to achieve better results than off-policy DPO. This iterative approach enhances the training process by sampling preference data more effectively .
  • Efficient Multimodal Learning: The paper emphasizes efficient multimodal learning from a data-centric perspective, focusing on improving the performance of large vision language models through self-training on image comprehension. This method enhances the learning process by incorporating image comprehension tasks .
  • Preference Distillation for Large Visual Language Models: The paper introduces the concept of preference distillation for large visual language models, aiming to improve model performance by distilling preferences into the training process. This method enhances the training of large visual language models by distilling preferences .
  • Self-Rewarding Language Models: The paper discusses self-rewarding language models as a method to optimize language model alignment. This approach involves models rewarding themselves during the training process, potentially improving alignment and performance .

These proposed ideas, methods, and models contribute to advancing the optimization and performance of multimodal large language models by addressing various challenges and introducing innovative approaches to preference optimization and model training. The proposed method, Conditional Preference Optimization (CoPO), in the paper introduces several key characteristics and advantages compared to previous methods in the field of multimodal large language models (LLMs) optimization :

  • Multimodal Focus: CoPO addresses the challenge of preference optimization neglecting images in multimodal scenarios, which is a crucial aspect often overlooked in previous methods .
  • Incorporation of Anchored Preference Optimization (AncPO): CoPO integrates AncPO into the objective of MDPO to prevent the likelihood of preferred responses from decreasing, which is a unique feature enhancing model performance .
  • Efficient Preference Learning: CoPO leverages conditional preference optimization to encourage multimodal LLMs to capture preference labels based on both visual and language cues, leading to more effective preference learning .
  • Reduction of Hallucination: The method effectively reduces hallucination across different model sizes on widely used benchmarks, demonstrating its ability to mitigate common issues in multimodal LLMs optimization .
  • Enhanced Model Performance: Experiments show that CoPO consistently enhances multimodal LLM performance compared to previous methods, particularly in scenarios where language-only preferences may not suffice, showcasing its superiority in optimizing multimodal models .
  • Overcoming Unconditional Preference Problem: CoPO specifically addresses the unconditional preference problem in multimodal preference optimization, ensuring that both visual and language cues are considered in the optimization process, leading to more accurate model alignment with human preferences .

These characteristics and advantages highlight the innovative approach of CoPO in improving multimodal large language models' optimization by effectively incorporating image preferences and enhancing model performance compared to previous methods.


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 papers exist in the field of multimodal large language models (LLMs) alignment and preference optimization. Noteworthy researchers in this field include Fei Wang, Wenxuan Zhou, James Y. Huang, Nan Xu, Sheng Zhang, Hoifung Poon, and Muhao Chen . These researchers have worked on methods like Direct Preference Optimization (DPO) and Multimodal DPO (MDPO) to align LLMs with human preferences and optimize model performance, particularly in reducing hallucinations .

The key to the solution mentioned in the paper is the introduction of MDPO, a multimodal DPO objective that prevents the over-prioritization of language-only preferences by also optimizing image preference. Additionally, the researchers propose a reward anchor that ensures positive rewards for chosen responses, thereby avoiding the decrease in their likelihood, which is a common issue in relative preference optimization . This approach effectively addresses the unconditional preference problem in multimodal preference optimization and significantly enhances model performance, especially in reducing hallucinations.


How were the experiments in the paper designed?

The experiments in the paper were designed with a specific structure:

  • The experimental setup involved applying MDPO on two multimodal LLMs of different sizes: Bunny-v1.0-3B and LLAVA-v1.5-7B. Bunny-v1.0-3B is a 3B model pretrained on 2M image-text pairs and finetuned on 1.3M instruction tuning data, while LLAVA-v1.5-7B is a 7B model pretrained on 558K image-text pairs and finetuned on 665K instruction tuning data .
  • Preference data sampling was done from Silkie with instructions from LLaVA-Instruct-150K for training. The original Silkie dataset contains 80K preference data collected on 12 multimodal LLMs. The preference data sampling involved using around 10K data for preference optimization .
  • The experiments evaluated the performance of MDPO on three widely used benchmarks for multimodal LLMs, focusing on hallucination. These benchmarks included MMHalBench, Object HalBench, and AMBER, assessing various metrics such as CHAIR scores, object coverage, hallucination rate, and cognition .

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

The dataset used for quantitative evaluation in the study is MMHalBench, which is a practical question answering benchmark containing eight question categories and 12 object topics . The code for the dataset is open source and can be accessed for further reference .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed to be verified. The study conducted a comprehensive analysis comparing the performance of MDPO (Conditional Preference Optimization) and standard DPO (Preference Optimization) on various benchmarks . The results indicate that MDPO outperforms standard DPO on six out of eight question categories, particularly excelling in scenarios involving adversarial questions with false premises about images . This demonstrates the effectiveness of MDPO in identifying incorrect information in questions based on images, a capability that standard DPO lacks .

Moreover, the paper reports that MDPO consistently outperforms DPO across different data scales, showing that the conditional preference method enhances multimodal preference optimization . The fine-grained results on MMHalBench also support the superiority of MDPO over DPO, with MDPO showing significant improvements in various practical scenarios . These findings provide strong empirical evidence in favor of the scientific hypotheses tested in the study, showcasing the effectiveness of MDPO in optimizing multimodal large language models .


What are the contributions of this paper?

The contributions of the paper include:

  • Introducing Conditional Preference Optimization for Multimodal Large Language Models (mDPO) .
  • Comparing the fine-grained results of DPO and MDPO on MMHalBench, showing that MDPO outperforms standard DPO on six out of eight question categories, particularly excelling in identifying incorrect information in questions based on images .

What work can be continued in depth?

Further research in the field of multimodal preference optimization can be expanded in several directions based on the limitations identified in the study:

  1. Experimentation with More Multimodal Large Language Models: Conducting experiments on a wider range of multimodal LLMs can provide additional insights into the strengths and weaknesses of the proposed method .
  2. Exploration of Complementary Perspectives: While the study focused on unconditional preference problems in multimodal preference optimization, future research could explore enhancing DPO from other perspectives, which may complement the current approach .
  3. Evaluation on a Diverse Range of Benchmarks: The study evaluated MDPO on three benchmarks, but further evaluation on a more extensive set of benchmarks can enhance the understanding of the proposed method in various real-world scenarios .

Introduction
Background
[A. Overview of DPO in multimodal models]
[B. Unconditional Preference Problem in LLMs]
Objective
[1. To identify and address the unconditional preference issue]
[2. Develop MDPO as a solution for improved model alignment]
[3. Minimize hallucinations in AI systems]
Method
Data Collection
[A. Selection of Bunny-v1.0-3B and LLaVA-v1.5-7B models]
[B. Benchmark datasets: MMHalBench, AMBER, and generative task]
Data Preprocessing
[1. Preparation of multimodal data for MDPO]
[2. Incorporation of reward anchor for positive reinforcement]
MDPO Algorithm
[A. Combining language and image preference optimization]
[B. Reward structure and its impact on model behavior]
Experiments and Results
[1. Performance comparison with DPO]
[2. Reduction in hallucinations and improved accuracy]
[3. Human evaluations for model superiority]
Future Work
Expansion and Model Diversity
[A. Testing MDPO on diverse models]
[B. Cross-model compatibility studies]
Integration with Other Methods
[1. Exploring combinations with preference enhancement techniques]
[2. Potential synergies and limitations]
Conclusion
[A. Summary of MDPO's impact on multimodal model alignment]
[B. Significance for reducing hallucinations in AI systems]
[C. Implications for future research in AI ethics and alignment]
Basic info
papers
computer vision and pattern recognition
computation and language
machine learning
artificial intelligence
Advanced features
Insights
What problem does Direct Preference Optimization (DPO) face when applied to multimodal large language models?
In human evaluations, what is the observed superiority of MDPO compared to DPO?
What is the proposed solution to the "unconditional preference problem" in DPO by the authors?
How does MDPO (Multimodal DPO) improve the performance of Bunny-v1.0-3B and LLaVA-v1.5-7B models, as shown in the experiments?

mDPO: Conditional Preference Optimization for Multimodal Large Language Models

Fei Wang, Wenxuan Zhou, James Y. Huang, Nan Xu, Sheng Zhang, Hoifung Poon, Muhao Chen·June 17, 2024

Summary

The paper investigates the challenges in applying Direct Preference Optimization (DPO) to multimodal large language models, specifically highlighting an "unconditional preference problem" where models ignore image conditions. To address this, the authors propose MDPO (Multimodal DPO), which combines language and image preference optimization, introducing a reward anchor to ensure positive rewards. Experiments with Bunny-v1.0-3B and LLaVA-v1.5-7B models on three benchmarks (MMHalBench, AMBER, and a generative task) show that MDPO effectively resolves the issue, improving model performance, reducing hallucinations, and even matching or outperforming DPO in some cases. Human evaluations further confirm MDPO's superiority. Future work suggests expanding the evaluation to diverse models and exploring combinations with other preference enhancement methods. The overall focus is on enhancing multimodal model alignment and reducing hallucinations in AI systems.
Mind map
[2. Potential synergies and limitations]
[1. Exploring combinations with preference enhancement techniques]
[B. Cross-model compatibility studies]
[A. Testing MDPO on diverse models]
[3. Human evaluations for model superiority]
[2. Reduction in hallucinations and improved accuracy]
[1. Performance comparison with DPO]
[B. Reward structure and its impact on model behavior]
[A. Combining language and image preference optimization]
[2. Incorporation of reward anchor for positive reinforcement]
[1. Preparation of multimodal data for MDPO]
[B. Benchmark datasets: MMHalBench, AMBER, and generative task]
[A. Selection of Bunny-v1.0-3B and LLaVA-v1.5-7B models]
[3. Minimize hallucinations in AI systems]
[2. Develop MDPO as a solution for improved model alignment]
[1. To identify and address the unconditional preference issue]
[B. Unconditional Preference Problem in LLMs]
[A. Overview of DPO in multimodal models]
[C. Implications for future research in AI ethics and alignment]
[B. Significance for reducing hallucinations in AI systems]
[A. Summary of MDPO's impact on multimodal model alignment]
Integration with Other Methods
Expansion and Model Diversity
Experiments and Results
MDPO Algorithm
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Future Work
Method
Introduction
Outline
Introduction
Background
[A. Overview of DPO in multimodal models]
[B. Unconditional Preference Problem in LLMs]
Objective
[1. To identify and address the unconditional preference issue]
[2. Develop MDPO as a solution for improved model alignment]
[3. Minimize hallucinations in AI systems]
Method
Data Collection
[A. Selection of Bunny-v1.0-3B and LLaVA-v1.5-7B models]
[B. Benchmark datasets: MMHalBench, AMBER, and generative task]
Data Preprocessing
[1. Preparation of multimodal data for MDPO]
[2. Incorporation of reward anchor for positive reinforcement]
MDPO Algorithm
[A. Combining language and image preference optimization]
[B. Reward structure and its impact on model behavior]
Experiments and Results
[1. Performance comparison with DPO]
[2. Reduction in hallucinations and improved accuracy]
[3. Human evaluations for model superiority]
Future Work
Expansion and Model Diversity
[A. Testing MDPO on diverse models]
[B. Cross-model compatibility studies]
Integration with Other Methods
[1. Exploring combinations with preference enhancement techniques]
[2. Potential synergies and limitations]
Conclusion
[A. Summary of MDPO's impact on multimodal model alignment]
[B. Significance for reducing hallucinations in AI systems]
[C. Implications for future research in AI ethics and alignment]
Key findings
4

Paper digest

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

The paper aims to address the unconditional preference problem in multimodal preference optimization, specifically in the context of large language models (LLMs) . This problem arises when applying Direct Preference Optimization (DPO) to multimodal LLMs, where the model overlooks the image condition in the preference dataset, leading to inconsistent improvements in model capabilities . The paper introduces a variant called DPO (No Image) to explore this issue, highlighting the challenge of effectively utilizing the visual modality in preference data . This problem is not entirely new, as recent studies have identified similar issues but have attributed them to the quality of preference data rather than the model's handling of the visual modality .


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate the scientific hypothesis related to multimodal preference optimization, specifically addressing the unconditional preference problem in multimodal preference optimization . The study aims to explore and propose a solution, MDPO (Multimodal Conditional Preference Optimization), to prevent the over-prioritization of language-only preferences by optimizing image preference as well, thereby improving model performance and reducing hallucination in multimodal large language models . The research focuses on enhancing the alignment of large language models with human preferences in multimodal scenarios, building on the success of Direct Preference Optimization (DPO) in the language modality .


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

The paper proposes several novel ideas, methods, and models in the field of multimodal large language models (LLMs) optimization:

  • Conditional Preference Optimization (CoPO): The paper introduces CoPO as a method to address the challenge of preference optimization neglecting images in multimodal scenarios. CoPO combines standard DPO, conditional preference optimization, and anchored preference optimization to enhance multimodal DPO .
  • Anchored Preference Optimization (AncPO): The paper incorporates AncPO as part of the objective of MDPO, which involves setting an anchor for the reward to be lower than a specific value. This approach aims to improve the optimization process by introducing anchors for rewards .
  • Iterative DPO and Sampling Preference Data: The paper discusses Iterative DPO and SPPO, which propose sampling preference data in an on-policy manner to achieve better results than off-policy DPO. This iterative approach enhances the training process by sampling preference data more effectively .
  • Efficient Multimodal Learning: The paper emphasizes efficient multimodal learning from a data-centric perspective, focusing on improving the performance of large vision language models through self-training on image comprehension. This method enhances the learning process by incorporating image comprehension tasks .
  • Preference Distillation for Large Visual Language Models: The paper introduces the concept of preference distillation for large visual language models, aiming to improve model performance by distilling preferences into the training process. This method enhances the training of large visual language models by distilling preferences .
  • Self-Rewarding Language Models: The paper discusses self-rewarding language models as a method to optimize language model alignment. This approach involves models rewarding themselves during the training process, potentially improving alignment and performance .

These proposed ideas, methods, and models contribute to advancing the optimization and performance of multimodal large language models by addressing various challenges and introducing innovative approaches to preference optimization and model training. The proposed method, Conditional Preference Optimization (CoPO), in the paper introduces several key characteristics and advantages compared to previous methods in the field of multimodal large language models (LLMs) optimization :

  • Multimodal Focus: CoPO addresses the challenge of preference optimization neglecting images in multimodal scenarios, which is a crucial aspect often overlooked in previous methods .
  • Incorporation of Anchored Preference Optimization (AncPO): CoPO integrates AncPO into the objective of MDPO to prevent the likelihood of preferred responses from decreasing, which is a unique feature enhancing model performance .
  • Efficient Preference Learning: CoPO leverages conditional preference optimization to encourage multimodal LLMs to capture preference labels based on both visual and language cues, leading to more effective preference learning .
  • Reduction of Hallucination: The method effectively reduces hallucination across different model sizes on widely used benchmarks, demonstrating its ability to mitigate common issues in multimodal LLMs optimization .
  • Enhanced Model Performance: Experiments show that CoPO consistently enhances multimodal LLM performance compared to previous methods, particularly in scenarios where language-only preferences may not suffice, showcasing its superiority in optimizing multimodal models .
  • Overcoming Unconditional Preference Problem: CoPO specifically addresses the unconditional preference problem in multimodal preference optimization, ensuring that both visual and language cues are considered in the optimization process, leading to more accurate model alignment with human preferences .

These characteristics and advantages highlight the innovative approach of CoPO in improving multimodal large language models' optimization by effectively incorporating image preferences and enhancing model performance compared to previous methods.


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 papers exist in the field of multimodal large language models (LLMs) alignment and preference optimization. Noteworthy researchers in this field include Fei Wang, Wenxuan Zhou, James Y. Huang, Nan Xu, Sheng Zhang, Hoifung Poon, and Muhao Chen . These researchers have worked on methods like Direct Preference Optimization (DPO) and Multimodal DPO (MDPO) to align LLMs with human preferences and optimize model performance, particularly in reducing hallucinations .

The key to the solution mentioned in the paper is the introduction of MDPO, a multimodal DPO objective that prevents the over-prioritization of language-only preferences by also optimizing image preference. Additionally, the researchers propose a reward anchor that ensures positive rewards for chosen responses, thereby avoiding the decrease in their likelihood, which is a common issue in relative preference optimization . This approach effectively addresses the unconditional preference problem in multimodal preference optimization and significantly enhances model performance, especially in reducing hallucinations.


How were the experiments in the paper designed?

The experiments in the paper were designed with a specific structure:

  • The experimental setup involved applying MDPO on two multimodal LLMs of different sizes: Bunny-v1.0-3B and LLAVA-v1.5-7B. Bunny-v1.0-3B is a 3B model pretrained on 2M image-text pairs and finetuned on 1.3M instruction tuning data, while LLAVA-v1.5-7B is a 7B model pretrained on 558K image-text pairs and finetuned on 665K instruction tuning data .
  • Preference data sampling was done from Silkie with instructions from LLaVA-Instruct-150K for training. The original Silkie dataset contains 80K preference data collected on 12 multimodal LLMs. The preference data sampling involved using around 10K data for preference optimization .
  • The experiments evaluated the performance of MDPO on three widely used benchmarks for multimodal LLMs, focusing on hallucination. These benchmarks included MMHalBench, Object HalBench, and AMBER, assessing various metrics such as CHAIR scores, object coverage, hallucination rate, and cognition .

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

The dataset used for quantitative evaluation in the study is MMHalBench, which is a practical question answering benchmark containing eight question categories and 12 object topics . The code for the dataset is open source and can be accessed for further reference .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed to be verified. The study conducted a comprehensive analysis comparing the performance of MDPO (Conditional Preference Optimization) and standard DPO (Preference Optimization) on various benchmarks . The results indicate that MDPO outperforms standard DPO on six out of eight question categories, particularly excelling in scenarios involving adversarial questions with false premises about images . This demonstrates the effectiveness of MDPO in identifying incorrect information in questions based on images, a capability that standard DPO lacks .

Moreover, the paper reports that MDPO consistently outperforms DPO across different data scales, showing that the conditional preference method enhances multimodal preference optimization . The fine-grained results on MMHalBench also support the superiority of MDPO over DPO, with MDPO showing significant improvements in various practical scenarios . These findings provide strong empirical evidence in favor of the scientific hypotheses tested in the study, showcasing the effectiveness of MDPO in optimizing multimodal large language models .


What are the contributions of this paper?

The contributions of the paper include:

  • Introducing Conditional Preference Optimization for Multimodal Large Language Models (mDPO) .
  • Comparing the fine-grained results of DPO and MDPO on MMHalBench, showing that MDPO outperforms standard DPO on six out of eight question categories, particularly excelling in identifying incorrect information in questions based on images .

What work can be continued in depth?

Further research in the field of multimodal preference optimization can be expanded in several directions based on the limitations identified in the study:

  1. Experimentation with More Multimodal Large Language Models: Conducting experiments on a wider range of multimodal LLMs can provide additional insights into the strengths and weaknesses of the proposed method .
  2. Exploration of Complementary Perspectives: While the study focused on unconditional preference problems in multimodal preference optimization, future research could explore enhancing DPO from other perspectives, which may complement the current approach .
  3. Evaluation on a Diverse Range of Benchmarks: The study evaluated MDPO on three benchmarks, but further evaluation on a more extensive set of benchmarks can enhance the understanding of the proposed method in various real-world scenarios .
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