Unveiling Safety Insights of Large Multimodal Models with GOAT-Bench

Основні поняття

Large multimodal models are evaluated for their ability to detect social abuse in memes using the GOAT-Bench dataset, revealing shortcomings in safety awareness and the need for further advancements in artificial intelligence.

Анотація

The exponential growth of social media has led to an increase in online abuse through memes. Large multimodal models are tested on the GOAT-Bench dataset to assess their ability to identify hatefulness, misogyny, offensiveness, sarcasm, and harmful content. Results show a deficiency in safety awareness among current models, emphasizing the importance of aligning AI with human values.
The study introduces GOAT-Bench, a comprehensive meme benchmark comprising over 6K varied memes focusing on implicit hate speech, sexism, and cyberbullying. The evaluation reveals that existing large multimodal models struggle with discerning nuanced forms of abuse present in memes. The research aims to contribute to enhancing safety insights in LMMs to prevent online social abuse escalation.
Various large multimodal models like GPT-4V, CogVLM, and LLaVA-1.5 are evaluated on tasks related to hatefulness, misogyny, offensiveness, sarcasm, and harmfulness using the GOAT-Bench dataset. Findings indicate disparities among models with GPT-4V showing the best overall performance but still exhibiting deficiencies in safety awareness.

Статистика

GPT-4V achieves an overall macro-averaged F1 score of 70.29%.
LLaVA-1.5 exhibits strong capabilities for detecting hatefulness.
Current models exhibit deficiencies in safety awareness.
The top-performing GPT-4V shows insensitivity to various forms of implicit abuse.

Цитати

"No model achieved a perfect score on all tasks."
"Results highlight the need for continued advancements in LMM safety."

Ключові висновки, отримані з

GOAT-Bench

by Hongzhan Lin... о arxiv.org 03-04-2024

https://arxiv.org/pdf/2401.01523.pdf

Глибші Запити

How can AI be aligned more closely with human values to improve safety insights?

To align AI more closely with human values and enhance safety insights, several key strategies can be implemented:

Ethical Guidelines: Establish clear ethical guidelines and principles for the development and deployment of AI systems. This includes ensuring transparency, fairness, accountability, and privacy in all AI applications.

Diverse Representation: Ensure diverse representation in the design and development of AI systems to mitigate biases and promote inclusivity. This involves incorporating perspectives from different cultural backgrounds, genders, ethnicities, etc.

Human-in-the-Loop Approaches: Implement human oversight mechanisms such as human-in-the-loop approaches where humans are involved in decision-making processes alongside AI systems to provide checks and balances.

Interpretability: Enhance the interpretability of AI models so that their decisions can be understood by humans. This helps build trust in the system's outputs and enables better alignment with societal values.

Continuous Monitoring: Regularly monitor the performance of AI systems post-deployment to identify any potential issues or biases that may arise over time. This ongoing evaluation ensures that the system remains aligned with evolving human values.

Collaboration with Experts: Collaborate with domain experts such as ethicists, sociologists, psychologists, etc., to incorporate their expertise into the development process and ensure alignment with ethical standards.

By implementing these strategies along with robust governance frameworks, organizations can improve the alignment of AI systems with human values and enhance safety insights across various applications.

What ethical considerations should be taken into account when developing large multimodal models?

When developing large multimodal models (LMMs), several ethical considerations must be taken into account to ensure responsible use:

Bias Mitigation: Proactively address biases present in training data by employing techniques like bias detection algorithms, data augmentation methods, or balanced dataset sampling to reduce unfair outcomes in model predictions.

Privacy Protection: Safeguard user privacy by anonymizing sensitive information during training datasets creation or implementing differential privacy techniques to prevent re-identification risks.

Transparency & Explainability: Strive for model transparency by providing explanations for model decisions through interpretable features or attention mechanisms so users understand how predictions are made.

4Fairness & Accountability: Incorporate fairness metrics during model evaluation stages to detect disparate impacts on different demographic groups; establish accountability measures within organizations for addressing harmful outcomes resulting from LMM deployments.
5Data Governance: Implement strict data governance policies regarding data collection practices; obtain explicit consent from individuals before using their data for training LMMs; adhere strictly to regulations like GDPR or CCPA concerning personal data protection.
6Social Impact Assessment: Conduct thorough social impact assessments prior deploying LMMs at scale; consider potential consequences on marginalized communities or vulnerable populations due biased predictions generated by these models
By integrating these ethical considerations throughout the development lifecycle of LMMs organizations can uphold moral standards while leveraging advanced technologies responsibly.

How can humor detection capabilities be improved in LMMs better understand meme-based social abuse?

Improving humor detection capabilities in Large Multimodal Models (LMMs) is crucial for understanding meme-based social abuse effectively:
1Training Data Augmentation: Expand training datasets include a wide range of humorous content across diverse cultures languages styles enable models learn recognize humor nuances accurately
2**Fine-tuning Strategies: Utilize fine-tuning techniques specifically tailored humor detection tasks optimize model performance detecting subtle comedic elements memes
3**Multimodal Fusion Techniques: Integrate advanced fusion methods combining image text modalities capture complex interplay between visual textual cues convey humor effectively
4**Contextual Understanding: Develop contextual understanding capabilities enable models grasp context surrounding jokes puns sarcasm memes enhancing accuracy identifying humorous content
5**Humor-Specific Pretraining: Consider pretraining strategies focus explicitly teaching models comprehend analyze humor language images prepare them better discerning comedic elements memes
6*Human-in-the-Loop Validation: Incorporate feedback loops involving humans validate model-generated responses related humor ensure accurate interpretation funny content avoid misinterpretations potentially offensive material
By implementing these strategies improving humor detection abilities LLMs become adept recognizing interpreting humorous elements memes thereby enhancing overall comprehension meme-based social abuse scenarios