Safety Pretraining: Toward the Next Generation of Safe AI
Project Overview
The document explores the role of generative AI in education, focusing on the integration of safety measures during the pretraining of large language models (LLMs) to mitigate the risks of harmful content generation. It highlights the necessity for proactive safety interventions, such as the development of a safety classifier, synthetic recontextualization of harmful data, refusal training, and harmfulness-tag annotations. These approaches aim to produce models that not only minimize the generation of harmful content but also align with ethical standards and human values. Furthermore, the document categorizes various types of harmful queries that AI models might generate, including violent and non-violent crimes, sexual offenses, child exploitation, defamation, privacy breaches, intellectual property violations, and hate speech. By emphasizing the implementation of robust safety measures and classifiers, the findings indicate a critical step towards creating safer AI applications in educational contexts, ensuring that generative AI can be harnessed effectively while reducing the potential for negative outcomes.
Key Applications
Harmful Content Mitigation and Ethical Education Framework
Context: Educational settings for developers, researchers, and students where AI models are used, focusing on teaching ethical principles and managing harmful content.
Implementation: A comprehensive data-centric approach that includes safety pretraining of AI models through data filtering, harmfulness-tag annotations, and the creation of dialogues that teach models to refuse harmful prompts. The implementation transforms harmful content into constructive moral education material.
Outcomes: Significant reduction in harmful content generation (from 38.8% to 8.4%) while enhancing the model's ability to navigate harmful content and providing students with responsible understanding of sensitive topics, thereby fostering ethical reasoning.
Challenges: Balancing the removal of harmful content with the retention of useful information and ensuring that educational materials remain engaging, informative, and sensitive to the subject matter. Additionally, developing realistic refusal scenarios that are both educational and effective.
Implementation Barriers
Technical & Content Safety
Difficulty in filtering harmful content without losing valuable educational information, including the challenge of generating inappropriate content by AI models.
Proposed Solutions: Utilizing safety classifiers and embedding models to detect and filter harmful content, along with synthetic recontextualization to preserve informative content while embedding it in a safe context.
Ethical
Risk of over-censoring important historical or scientific information while ensuring safety.
Proposed Solutions: Careful rephrasing and context injection to maintain the educational value of sensitive topics.
Implementation
Logistical challenges in creating and maintaining comprehensive datasets that are safe and effective.
Proposed Solutions: Utilization of a variety of datasets and approaches, such as RefuseWeb and Moral Education datasets, to cover diverse scenarios.
Project Team
Pratyush Maini
Researcher
Sachin Goyal
Researcher
Dylan Sam
Researcher
Alex Robey
Researcher
Yash Savani
Researcher
Yiding Jiang
Researcher
Andy Zou
Researcher
Zacharcy C. Lipton
Researcher
J. Zico Kolter
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Pratyush Maini, Sachin Goyal, Dylan Sam, Alex Robey, Yash Savani, Yiding Jiang, Andy Zou, Zacharcy C. Lipton, J. Zico Kolter
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai