Responsible Generation of Textual and Visual Content: Addressing Hallucination, Toxicity, and Adversarial Vulnerabilities

Основные понятия

Responsible generation of content by generative AI models is crucial for their real-world applicability. This paper investigates the practical responsible requirements of both textual and visual generative models, outlining key considerations such as generating truthful content, avoiding toxic content, refusing harmful instructions, protecting training data privacy, and ensuring generated content identifiability.

Аннотация

The paper discusses the responsible requirements for both textual and visual generative AI models. It covers the following key points:

1. Generating Truthful Content:

   • Hallucination in language models (LMs) refers to generating content that is nonsensical or unfaithful to the provided source content. Researchers have investigated the causes of hallucination, including biases in training data, modeling approaches, and inference processes.
   • Techniques for detecting and mitigating hallucination have been explored, such as using external knowledge, model uncertainty, and multi-round self-evaluation.
   • Visual hallucination in multimodal LMs, where the generated text does not align with the input images, has also been studied. Causes include over-reliance on language priors and biases in training data.

2. Avoiding Toxic Content:

   • LMs can generate various types of toxic outputs, including social biases, offensive content, and personally identifiable information.
   • Techniques for discovering, measuring, and mitigating toxic generation have been proposed, such as using adversarial models for red teaming, toxicity scoring, and prompt engineering.

3. Refusing Harmful Instructions:

   • Adversarial attacks, such as prompt injection, prompt extraction, jailbreak, and backdoor attacks, can induce LMs to generate inappropriate or harmful content.
   • Researchers have explored methods to defend against these attacks, including adversarial training and detection.

4. Protecting Training Data Privacy:

   • Recent research has shown that the learned parameters of large-scale generative models can contain information about their training instances, posing privacy concerns.
   • Techniques to extract training data from pre-trained models and methods to hide the training data information have been studied.

5. Ensuring Generated Content Identifiability:

   • The copyright and attribution of generated content is a complex problem that requires knowledge from multiple disciplines.
   • Approaches for generating detectable content (e.g., with watermarks) and model attribution (i.e., identifying which model generated a particular instance) have been explored.

The paper emphasizes the importance of responsible generative AI across various domains, including healthcare, education, finance, and artificial general intelligence. It provides insights into practical safety-related issues and aims to benefit the community in building responsible generative AI.

Статистика

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Цитаты

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Ключевые выводы из

Responsible Generative AI

by Jindong Gu в arxiv.org 04-10-2024

https://arxiv.org/pdf/2404.05783.pdf

Дополнительные вопросы

How can we ensure that responsible generative AI models maintain their safety and reliability as they become more capable and widely deployed?

As responsible generative AI models become more advanced and widely deployed, ensuring their safety and reliability is crucial. Several strategies can be implemented to maintain the integrity of these models:

Robust Testing and Validation: Implement thorough testing and validation procedures to identify and mitigate potential risks and vulnerabilities in the models. This includes testing for biases, ethical considerations, and unintended consequences.

Continuous Monitoring: Establish monitoring systems to track the performance of generative AI models in real-time. This allows for the detection of any deviations from expected behavior and enables prompt intervention if issues arise.

Transparency and Explainability: Promote transparency in the development and deployment of generative AI models by providing clear explanations of how the models operate and make decisions. This helps build trust with users and stakeholders.

Ethical Guidelines and Governance: Develop and adhere to ethical guidelines and governance frameworks that outline the responsible use of generative AI. This includes setting clear boundaries on the type of content that can be generated and ensuring compliance with legal and ethical standards.

User Feedback and Input: Incorporate user feedback and input into the model development process to address concerns and improve the overall performance and safety of the models.

Regular Updates and Maintenance: Keep generative AI models up to date with the latest advancements in technology and best practices. Regular maintenance and updates help address emerging threats and vulnerabilities.

By implementing these strategies, responsible generative AI models can maintain their safety and reliability as they continue to evolve and become more prevalent in various applications.

What are the potential unintended consequences of responsible generative AI, and how can we proactively address them?

While responsible generative AI models offer numerous benefits, there are potential unintended consequences that need to be addressed:

Bias and Discrimination: Generative AI models can inadvertently perpetuate biases present in the training data, leading to discriminatory outcomes. To address this, it is essential to implement bias detection and mitigation techniques during model development.

Misinformation and Manipulation: There is a risk of generative AI models being used to spread misinformation or manipulate information. Proactive measures such as fact-checking, content verification, and transparency in model outputs can help mitigate this risk.

Privacy Concerns: Generative AI models may inadvertently generate content that compromises user privacy by revealing sensitive information. Implementing data protection measures, anonymization techniques, and secure data handling practices can help address privacy concerns.

Security Vulnerabilities: Generative AI models can be vulnerable to adversarial attacks, where malicious actors manipulate the model's outputs. Robust security measures, such as adversarial training and model hardening, can help mitigate these risks.

Legal and Ethical Challenges: Responsible generative AI may raise legal and ethical challenges related to intellectual property rights, copyright infringement, and ethical use of AI technology. Adhering to legal frameworks, ethical guidelines, and industry standards can help navigate these challenges.

To proactively address these unintended consequences, it is essential to prioritize ethical considerations, engage with diverse stakeholders, conduct thorough risk assessments, and continuously evaluate and update the models to ensure they align with ethical and societal norms.

How can the principles of responsible generative AI be extended to other emerging AI technologies, such as reinforcement learning or multi-agent systems?

Extending the principles of responsible generative AI to other emerging AI technologies like reinforcement learning and multi-agent systems involves applying similar ethical considerations and best practices:

Ethical Frameworks: Develop ethical frameworks and guidelines specific to reinforcement learning and multi-agent systems to ensure responsible development and deployment of these technologies.

Transparency and Accountability: Promote transparency and accountability in the design and implementation of reinforcement learning algorithms and multi-agent systems. This includes explaining the decision-making processes and ensuring traceability of actions.

Bias Detection and Mitigation: Implement bias detection and mitigation strategies to address potential biases in reinforcement learning models and multi-agent systems. This involves identifying and correcting biases in the training data and decision-making processes.

Fairness and Equity: Ensure fairness and equity in the outcomes produced by reinforcement learning algorithms and multi-agent systems. This includes monitoring for discriminatory behavior and taking corrective actions to promote fairness.

Data Privacy and Security: Prioritize data privacy and security in the development of reinforcement learning and multi-agent systems. Implement robust data protection measures, encryption techniques, and access controls to safeguard sensitive information.

Collaborative Governance: Foster collaborative governance structures that involve stakeholders from diverse backgrounds in the decision-making processes related to reinforcement learning and multi-agent systems. This helps ensure that the technology aligns with societal values and norms.

By extending the principles of responsible generative AI to other emerging AI technologies, we can promote the ethical and responsible use of these technologies while mitigating potential risks and ensuring positive societal impact.