SPA-VL: A Comprehensive Safety Preference Alignment Dataset for Vision Language Model

Yongting Zhang, Lu Chen, Guodong Zheng, Yifeng Gao, Rui Zheng, Jinlan Fu, Zhenfei Yin, Senjie Jin, Yu Qiao, Xuanjing Huang, Feng Zhao, Tao Gui, Jing Shao·June 17, 2024

Summary

The paper presents SPA-VL, a comprehensive dataset for aligning Vision Language Models (VLMs) with human safety. It covers 6 harm domains, 13 categories, and 53 subcategories, containing 100,788 samples, ensuring diversity through open-source and closed-source models. Experiments show that training on SPA-VL improves harmlessness and helpfulness without compromising core capabilities. The dataset contributes to ensuring VLMs are both safe and helpful, with publicly available code and data. The study also evaluates the impact of data scale, diversity, and question types on alignment effectiveness, demonstrating the benefits of larger datasets like SPA-VL for robust safety in VLMs.

Key findings

7

Paper digest

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

The paper aims to address the issue of safety preference alignment in Vision Language Models (VLMs) by creating a dataset called SPA-VL that focuses on harmlessness and helpfulness criteria . This problem is not entirely new but represents a significant challenge in ensuring VLMs align with human values and exhibit safe behavior when generating responses . The dataset construction process and model training detailed in the paper emphasize the importance of incorporating both textual and visual components to enhance safety alignment in VLMs .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to the effectiveness of safety alignment techniques in Vision Language Models (VLMs) by introducing a comprehensive dataset called SPA-VL. The hypothesis focuses on aligning VLMs with human values, specifically emphasizing harmlessness, helpfulness, and honesty . The study evaluates the performance of models trained on the SPA-VL dataset, such as LLaVA-SPA-VL-DPO and LLaVA-SPA-VL-PPO, which demonstrate superior safety performance compared to baseline models and open-source models across various safety metrics . The research also delves into the impact of dataset scale on alignment model performance, showcasing insights on how varying data quantities influence safety metrics like Harm Score, Attack Success Rate (ASR), and Help Score .


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

The paper "SPA-VL: A Comprehensive Safety Preference Alignment Dataset for Vision Language Model" introduces several innovative ideas, methods, and models in the field of aligning Vision Language Models (VLMs) with human values . Here are the key contributions:

  1. Dataset Construction: The paper details the construction process of the SPA-VL dataset, which involves gathering preference data by selecting the better response from two generated by VLMs based on predefined criteria of harmlessness and helpfulness. The dataset consists of quadruples reflecting preferences (question, image, chosen response, rejected response) .

  2. Model Performance: The study evaluates different open-source models and various dataset-trained models on harmlessness. The models are assessed across multiple metrics on MM-SafetyBench, AdvBench, and HarmEval UnSafe Rate. The models trained on the SPA-VL dataset achieve the best scores across all metrics using both dpo and ppo methods .

  3. Alignment Model Performance: The impact of data scale on alignment model performance is analyzed. The study delves into the effect of varying data quantities on alignment models, showcasing insights such as the Harm Score decreasing with increasing data volume and the Average Attack Success Rate (ASR) declining as data scale grows. The help score exhibits a progressive increase as the dataset size expands, indicating an enhancement in safety and helpfulness .

  4. Safety-Aligned Models: The paper introduces safety-aligned models, LLaVA-SPA-VL-DPO, and LLaVA-SPA-VL-PPO, which exhibit superior safety performance compared to baseline models and other open-source models. These models achieve the best safe results on various benchmarks like MM-SafetyBench, AdvBench, and HarmEval tests .

  5. Future Directions: The paper outlines future directions, aiming to extend the work to encompass a unified "3H" framework of helpfulness, harmlessness, and honesty for aligning VLMs with human values. Additionally, the study plans to explore the application of safety alignment techniques to more complex tasks such as reasoning in VLMs and investigate the transferability of alignment capabilities between different modalities . The paper "SPA-VL: A Comprehensive Safety Preference Alignment Dataset for Vision Language Model" introduces novel characteristics and advantages compared to previous methods in aligning Vision Language Models (VLMs) with human values .

  6. Dataset Construction: The SPA-VL dataset is constructed by gathering preference data based on predefined criteria of harmlessness and helpfulness, resulting in quadruples reflecting preferences (question, image, chosen response, rejected response). This dataset construction process ensures a robust categorization framework and leverages the CLIP model for matching images and text effectively, aligning images with harm categories accurately .

  7. Model Performance: The study evaluates various open-source models and dataset-trained models on harmlessness, showcasing that models trained on the SPA-VL dataset achieve the best scores across multiple metrics on MM-SafetyBench, AdvBench, and HarmEval UnSafe Rate. The safety-aligned models, LLaVA-SPA-VL-DPO and LLaVA-SPA-VL-PPO, exhibit superior safety performance compared to baseline models and other open-source models, achieving the best safe results on various benchmarks .

  8. Impact of Data Scale: The paper delves into the impact of data scale on alignment model performance, revealing insights such as the Harm Score decreasing with increasing data volume, the Average Attack Success Rate (ASR) declining as data scale grows, and the Help Score exhibiting a progressive increase as the dataset size expands. This demonstrates that as the dataset size grows, there is a simultaneous enhancement in safety and helpfulness, highlighting the importance of comprehensive datasets like SPA-VL .

  9. General Ability and Robustness: The safety-aligned models in the paper showcase robust general ability across various safety metrics without a significant decline compared to the backbone model. The incorporation of image data into safety alignment datasets is emphasized as crucial for VLMs, as models trained solely on language-based datasets may experience performance drops in safety tests involving images. This underscores the importance of multimodal datasets like SPA-VL for robust safety alignment in VLMs .

In summary, the characteristics and advantages of the SPA-VL dataset and safety-aligned models lie in their robust dataset construction, superior model performance, scalability with data size, and the ability to maintain general performance across various safety metrics, emphasizing the critical role of multimodal data in training safe and reliable VLMs .


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?

In the field related to safety preference alignment datasets for Vision Language Models (VLMs), there are several noteworthy researchers and key solutions mentioned in the paper . Some of the notable researchers in this field include Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, and many others . The key solution highlighted in the paper involves the creation of comprehensive datasets like SPA-VL, which incorporate both textual and visual components to ensure robust safety alignment in VLMs . This approach aims to enhance safety and helpfulness simultaneously by leveraging multimodal data and training models that exhibit superior safety performance across various metrics .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the safety performance of models trained on the SPA-VL dataset. The experiments involved training models on the proposed dataset and assessing their performance across various metrics such as MM-SafetyBench, AdvBench, and HarmEval UnSafe Rate (HarmEval USR) . The models trained on the SPA-VL dataset, specifically LLaVA-SPA-VL-DPO and LLaVA-SPA-VL-PPO, demonstrated superior safety performance compared to baseline models and other open-source models . Additionally, the impact of data scale on alignment model performance was explored by varying data quantities and conducting experiments across different evaluation metrics . The results highlighted the importance of comprehensive datasets like SPA-VL, which include both textual and visual components, in training safe and reliable Vision-Language Models (VLMs) .


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

The dataset used for quantitative evaluation in the study is the SPA-VL dataset . The code for the dataset is open source, as indicated by the reference to different open-source models in the comparison table .


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 verification. The study conducted comprehensive evaluations across various metrics and datasets to assess the safety performance of alignment models trained on the SPA-VL dataset . The models trained on SPA-VL, specifically LLaVA-SPA-VL-DPO and LLaVA-SPA-VL-PPO, demonstrated superior safety performance compared to baseline models and other open-source models, showcasing their effectiveness in achieving the best safety results on MM-SafetyBench, AdvBench, and HarmEval tests .

Furthermore, the analysis delved into the impact of data scale on alignment model performance, revealing insightful trends across different data quantities . The experiments showed that as the data volume increased, there was a consistent decrease in Harm Score and Average Attack Success Rate (ASR), while the Help Score exhibited a progressive increase, indicating an enhancement in safety and helpfulness with larger dataset sizes . These findings provide robust evidence supporting the hypotheses related to the effectiveness of data scale in improving alignment model performance and safety metrics.

Overall, the detailed experiments, comparisons, and results presented in the paper offer substantial empirical evidence to validate the scientific hypotheses regarding safety alignment in Vision Language Models (VLMs) using the SPA-VL dataset. The study's thorough evaluation across multiple benchmarks and metrics establishes a strong foundation for the effectiveness of the proposed safety alignment techniques and the critical role of multimodal data in training safe and reliable VLMs .


What are the contributions of this paper?

The paper makes several key contributions:

  • It introduces the SPA-VL dataset, which aligns safety preferences for Vision Language Models (VLMs) by selecting the better response based on predefined harmlessness and helpfulness criteria .
  • The paper evaluates different open-source models and dataset-trained models on harmlessness, demonstrating that models trained on the SPA-VL dataset achieve the best scores across various metrics on MM-SafetyBench, AdvBench, and HarmEval tests .
  • The study presents the impact of data scale on alignment model performance, showing that increasing data volume leads to improvements in safety and helpfulness, as indicated by the help score in Anthropic-Helpful .
  • Additionally, the paper discusses future directions, aiming to extend the alignment framework to encompass helpfulness, harmlessness, and honesty, explore safety alignment in more complex VLM tasks, and investigate alignment transferability between different modalities .

What work can be continued in depth?

The work can be continued in depth by expanding the scope to encompass the unified "3H" framework of helpfulness, harmlessness, and honesty to ensure a more holistic approach to aligning Vision and Language Models (VLMs) with human values . Additionally, further exploration can be done on the application of safety alignment techniques to more complex tasks such as reasoning in VLMs, which would require a nuanced understanding and generation of visual content . Investigating the transferability of alignment capabilities between Large Language Models (LLMs) and VLMs could also be a valuable area to explore, potentially leading to more efficient and effective alignment strategies across different modalities .

Tables

17

Introduction
Background
Overview of Vision Language Models (VLMs) and their increasing role in AI
Importance of safety considerations in VLMs
Objective
To introduce SPA-VL dataset
Aim to improve harmlessness and helpfulness in VLMs without compromising core functionality
Focus on harm domains, categories, and subcategories
Method
Data Collection
Domain Selection
6 harm domains: Physical, Emotional, Social, Legal, Environmental, and Ethical
Category and Subcategory Formation
13 main categories and 53 subcategories for diverse scenarios
Data Sources
Open-source and closed-source models for sample generation
Data Preparation
Sample Generation
100,788 samples, ensuring variety and relevance
Data Annotation
Human annotation for harmlessness, helpfulness, and core capabilities
Experiment Design
Training VLMs on SPA-VL dataset
Evaluation metrics: harmlessness, helpfulness, and core functionality
Experiments and Results
Impact of SPA-VL
Improved performance in harmlessness and helpfulness
Maintained core capabilities
Data Scale Analysis
Effect of dataset size on alignment effectiveness
SPA-VL's contribution to robust safety
Diversity Analysis
Importance of diverse data in aligning VLMs
SPA-VL's role in promoting diverse scenarios
Question Types
Influence of different question types on alignment
SPA-VL's versatility in handling various query styles
Conclusion
Public availability of code and data for replication and further research
SPA-VL as a valuable resource for ensuring safe and helpful VLMs
Future Directions
Potential for future research and dataset expansion
Addressing remaining challenges in VLM safety and alignment
Basic info
papers
computer vision and pattern recognition
computation and language
artificial intelligence
Advanced features
Insights
How many harm domains, categories, and subcategories does SPA-VL cover?
What is the primary focus of the SPA-VL dataset?
What improvements does training on SPA-VL bring to Vision Language Models in terms of safety and helpfulness?
How many samples are included in the SPA-VL dataset, and what ensures its diversity?

SPA-VL: A Comprehensive Safety Preference Alignment Dataset for Vision Language Model

Yongting Zhang, Lu Chen, Guodong Zheng, Yifeng Gao, Rui Zheng, Jinlan Fu, Zhenfei Yin, Senjie Jin, Yu Qiao, Xuanjing Huang, Feng Zhao, Tao Gui, Jing Shao·June 17, 2024

Summary

The paper presents SPA-VL, a comprehensive dataset for aligning Vision Language Models (VLMs) with human safety. It covers 6 harm domains, 13 categories, and 53 subcategories, containing 100,788 samples, ensuring diversity through open-source and closed-source models. Experiments show that training on SPA-VL improves harmlessness and helpfulness without compromising core capabilities. The dataset contributes to ensuring VLMs are both safe and helpful, with publicly available code and data. The study also evaluates the impact of data scale, diversity, and question types on alignment effectiveness, demonstrating the benefits of larger datasets like SPA-VL for robust safety in VLMs.
Mind map
Human annotation for harmlessness, helpfulness, and core capabilities
100,788 samples, ensuring variety and relevance
Open-source and closed-source models for sample generation
13 main categories and 53 subcategories for diverse scenarios
6 harm domains: Physical, Emotional, Social, Legal, Environmental, and Ethical
SPA-VL's versatility in handling various query styles
Influence of different question types on alignment
SPA-VL's role in promoting diverse scenarios
Importance of diverse data in aligning VLMs
SPA-VL's contribution to robust safety
Effect of dataset size on alignment effectiveness
Maintained core capabilities
Improved performance in harmlessness and helpfulness
Evaluation metrics: harmlessness, helpfulness, and core functionality
Training VLMs on SPA-VL dataset
Data Annotation
Sample Generation
Data Sources
Category and Subcategory Formation
Domain Selection
Focus on harm domains, categories, and subcategories
Aim to improve harmlessness and helpfulness in VLMs without compromising core functionality
To introduce SPA-VL dataset
Importance of safety considerations in VLMs
Overview of Vision Language Models (VLMs) and their increasing role in AI
Addressing remaining challenges in VLM safety and alignment
Potential for future research and dataset expansion
SPA-VL as a valuable resource for ensuring safe and helpful VLMs
Public availability of code and data for replication and further research
Question Types
Diversity Analysis
Data Scale Analysis
Impact of SPA-VL
Experiment Design
Data Preparation
Data Collection
Objective
Background
Future Directions
Conclusion
Experiments and Results
Method
Introduction
Outline
Introduction
Background
Overview of Vision Language Models (VLMs) and their increasing role in AI
Importance of safety considerations in VLMs
Objective
To introduce SPA-VL dataset
Aim to improve harmlessness and helpfulness in VLMs without compromising core functionality
Focus on harm domains, categories, and subcategories
Method
Data Collection
Domain Selection
6 harm domains: Physical, Emotional, Social, Legal, Environmental, and Ethical
Category and Subcategory Formation
13 main categories and 53 subcategories for diverse scenarios
Data Sources
Open-source and closed-source models for sample generation
Data Preparation
Sample Generation
100,788 samples, ensuring variety and relevance
Data Annotation
Human annotation for harmlessness, helpfulness, and core capabilities
Experiment Design
Training VLMs on SPA-VL dataset
Evaluation metrics: harmlessness, helpfulness, and core functionality
Experiments and Results
Impact of SPA-VL
Improved performance in harmlessness and helpfulness
Maintained core capabilities
Data Scale Analysis
Effect of dataset size on alignment effectiveness
SPA-VL's contribution to robust safety
Diversity Analysis
Importance of diverse data in aligning VLMs
SPA-VL's role in promoting diverse scenarios
Question Types
Influence of different question types on alignment
SPA-VL's versatility in handling various query styles
Conclusion
Public availability of code and data for replication and further research
SPA-VL as a valuable resource for ensuring safe and helpful VLMs
Future Directions
Potential for future research and dataset expansion
Addressing remaining challenges in VLM safety and alignment
Key findings
7

Paper digest

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

The paper aims to address the issue of safety preference alignment in Vision Language Models (VLMs) by creating a dataset called SPA-VL that focuses on harmlessness and helpfulness criteria . This problem is not entirely new but represents a significant challenge in ensuring VLMs align with human values and exhibit safe behavior when generating responses . The dataset construction process and model training detailed in the paper emphasize the importance of incorporating both textual and visual components to enhance safety alignment in VLMs .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to the effectiveness of safety alignment techniques in Vision Language Models (VLMs) by introducing a comprehensive dataset called SPA-VL. The hypothesis focuses on aligning VLMs with human values, specifically emphasizing harmlessness, helpfulness, and honesty . The study evaluates the performance of models trained on the SPA-VL dataset, such as LLaVA-SPA-VL-DPO and LLaVA-SPA-VL-PPO, which demonstrate superior safety performance compared to baseline models and open-source models across various safety metrics . The research also delves into the impact of dataset scale on alignment model performance, showcasing insights on how varying data quantities influence safety metrics like Harm Score, Attack Success Rate (ASR), and Help Score .


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

The paper "SPA-VL: A Comprehensive Safety Preference Alignment Dataset for Vision Language Model" introduces several innovative ideas, methods, and models in the field of aligning Vision Language Models (VLMs) with human values . Here are the key contributions:

  1. Dataset Construction: The paper details the construction process of the SPA-VL dataset, which involves gathering preference data by selecting the better response from two generated by VLMs based on predefined criteria of harmlessness and helpfulness. The dataset consists of quadruples reflecting preferences (question, image, chosen response, rejected response) .

  2. Model Performance: The study evaluates different open-source models and various dataset-trained models on harmlessness. The models are assessed across multiple metrics on MM-SafetyBench, AdvBench, and HarmEval UnSafe Rate. The models trained on the SPA-VL dataset achieve the best scores across all metrics using both dpo and ppo methods .

  3. Alignment Model Performance: The impact of data scale on alignment model performance is analyzed. The study delves into the effect of varying data quantities on alignment models, showcasing insights such as the Harm Score decreasing with increasing data volume and the Average Attack Success Rate (ASR) declining as data scale grows. The help score exhibits a progressive increase as the dataset size expands, indicating an enhancement in safety and helpfulness .

  4. Safety-Aligned Models: The paper introduces safety-aligned models, LLaVA-SPA-VL-DPO, and LLaVA-SPA-VL-PPO, which exhibit superior safety performance compared to baseline models and other open-source models. These models achieve the best safe results on various benchmarks like MM-SafetyBench, AdvBench, and HarmEval tests .

  5. Future Directions: The paper outlines future directions, aiming to extend the work to encompass a unified "3H" framework of helpfulness, harmlessness, and honesty for aligning VLMs with human values. Additionally, the study plans to explore the application of safety alignment techniques to more complex tasks such as reasoning in VLMs and investigate the transferability of alignment capabilities between different modalities . The paper "SPA-VL: A Comprehensive Safety Preference Alignment Dataset for Vision Language Model" introduces novel characteristics and advantages compared to previous methods in aligning Vision Language Models (VLMs) with human values .

  6. Dataset Construction: The SPA-VL dataset is constructed by gathering preference data based on predefined criteria of harmlessness and helpfulness, resulting in quadruples reflecting preferences (question, image, chosen response, rejected response). This dataset construction process ensures a robust categorization framework and leverages the CLIP model for matching images and text effectively, aligning images with harm categories accurately .

  7. Model Performance: The study evaluates various open-source models and dataset-trained models on harmlessness, showcasing that models trained on the SPA-VL dataset achieve the best scores across multiple metrics on MM-SafetyBench, AdvBench, and HarmEval UnSafe Rate. The safety-aligned models, LLaVA-SPA-VL-DPO and LLaVA-SPA-VL-PPO, exhibit superior safety performance compared to baseline models and other open-source models, achieving the best safe results on various benchmarks .

  8. Impact of Data Scale: The paper delves into the impact of data scale on alignment model performance, revealing insights such as the Harm Score decreasing with increasing data volume, the Average Attack Success Rate (ASR) declining as data scale grows, and the Help Score exhibiting a progressive increase as the dataset size expands. This demonstrates that as the dataset size grows, there is a simultaneous enhancement in safety and helpfulness, highlighting the importance of comprehensive datasets like SPA-VL .

  9. General Ability and Robustness: The safety-aligned models in the paper showcase robust general ability across various safety metrics without a significant decline compared to the backbone model. The incorporation of image data into safety alignment datasets is emphasized as crucial for VLMs, as models trained solely on language-based datasets may experience performance drops in safety tests involving images. This underscores the importance of multimodal datasets like SPA-VL for robust safety alignment in VLMs .

In summary, the characteristics and advantages of the SPA-VL dataset and safety-aligned models lie in their robust dataset construction, superior model performance, scalability with data size, and the ability to maintain general performance across various safety metrics, emphasizing the critical role of multimodal data in training safe and reliable VLMs .


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?

In the field related to safety preference alignment datasets for Vision Language Models (VLMs), there are several noteworthy researchers and key solutions mentioned in the paper . Some of the notable researchers in this field include Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, and many others . The key solution highlighted in the paper involves the creation of comprehensive datasets like SPA-VL, which incorporate both textual and visual components to ensure robust safety alignment in VLMs . This approach aims to enhance safety and helpfulness simultaneously by leveraging multimodal data and training models that exhibit superior safety performance across various metrics .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the safety performance of models trained on the SPA-VL dataset. The experiments involved training models on the proposed dataset and assessing their performance across various metrics such as MM-SafetyBench, AdvBench, and HarmEval UnSafe Rate (HarmEval USR) . The models trained on the SPA-VL dataset, specifically LLaVA-SPA-VL-DPO and LLaVA-SPA-VL-PPO, demonstrated superior safety performance compared to baseline models and other open-source models . Additionally, the impact of data scale on alignment model performance was explored by varying data quantities and conducting experiments across different evaluation metrics . The results highlighted the importance of comprehensive datasets like SPA-VL, which include both textual and visual components, in training safe and reliable Vision-Language Models (VLMs) .


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

The dataset used for quantitative evaluation in the study is the SPA-VL dataset . The code for the dataset is open source, as indicated by the reference to different open-source models in the comparison table .


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 verification. The study conducted comprehensive evaluations across various metrics and datasets to assess the safety performance of alignment models trained on the SPA-VL dataset . The models trained on SPA-VL, specifically LLaVA-SPA-VL-DPO and LLaVA-SPA-VL-PPO, demonstrated superior safety performance compared to baseline models and other open-source models, showcasing their effectiveness in achieving the best safety results on MM-SafetyBench, AdvBench, and HarmEval tests .

Furthermore, the analysis delved into the impact of data scale on alignment model performance, revealing insightful trends across different data quantities . The experiments showed that as the data volume increased, there was a consistent decrease in Harm Score and Average Attack Success Rate (ASR), while the Help Score exhibited a progressive increase, indicating an enhancement in safety and helpfulness with larger dataset sizes . These findings provide robust evidence supporting the hypotheses related to the effectiveness of data scale in improving alignment model performance and safety metrics.

Overall, the detailed experiments, comparisons, and results presented in the paper offer substantial empirical evidence to validate the scientific hypotheses regarding safety alignment in Vision Language Models (VLMs) using the SPA-VL dataset. The study's thorough evaluation across multiple benchmarks and metrics establishes a strong foundation for the effectiveness of the proposed safety alignment techniques and the critical role of multimodal data in training safe and reliable VLMs .


What are the contributions of this paper?

The paper makes several key contributions:

  • It introduces the SPA-VL dataset, which aligns safety preferences for Vision Language Models (VLMs) by selecting the better response based on predefined harmlessness and helpfulness criteria .
  • The paper evaluates different open-source models and dataset-trained models on harmlessness, demonstrating that models trained on the SPA-VL dataset achieve the best scores across various metrics on MM-SafetyBench, AdvBench, and HarmEval tests .
  • The study presents the impact of data scale on alignment model performance, showing that increasing data volume leads to improvements in safety and helpfulness, as indicated by the help score in Anthropic-Helpful .
  • Additionally, the paper discusses future directions, aiming to extend the alignment framework to encompass helpfulness, harmlessness, and honesty, explore safety alignment in more complex VLM tasks, and investigate alignment transferability between different modalities .

What work can be continued in depth?

The work can be continued in depth by expanding the scope to encompass the unified "3H" framework of helpfulness, harmlessness, and honesty to ensure a more holistic approach to aligning Vision and Language Models (VLMs) with human values . Additionally, further exploration can be done on the application of safety alignment techniques to more complex tasks such as reasoning in VLMs, which would require a nuanced understanding and generation of visual content . Investigating the transferability of alignment capabilities between Large Language Models (LLMs) and VLMs could also be a valuable area to explore, potentially leading to more efficient and effective alignment strategies across different modalities .

Tables
17
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