The Safety Trade-offs of Advanced AI: Insights from Llama-3.3 and Tulu-3
Alongside major closed-source model announcements in late 2024, the open-source community also saw key releases. In this brief post, we explore Llama-3.3 and Tulu-3, evaluating their performance in terms of AI safety and security.
With a group of major announcements made from close-source frontier models at the end of 2024, there were also notable releases in the open-source community. In this short blog post, we delve into two of the latest open-source models, Llama-3.3 and Tulu-3, to evaluate their performance from the perspectives of AI Safety and Security.
What is Llama-3.3?
Llama-3.3 is a text-only, 70-billion parameter instruction-tuned model developed by Meta. Released on December 6, 2024, it represents a significant improvement over its predecessor, Llama-3.1, and even outperforms larger models like Llama-3.2 90B for text-only applications. Designed specifically for multilingual dialogue, Llama-3.3 supports languages such as English, German, French, Spanish, and Hindi, among others.
The model employs a refined transformer architecture with Supervised Fine-Tuning (SFT) and Reinforcement Learning with Human Feedback (RLHF) to align outputs with user preferences for safety and helpfulness. Key features include a 128k token context length and optimizations like Grouped-Query Attention (GQA) for scalable inference. Despite its focus on safety, our evaluations reveal vulnerabilities to adversarial attacks, emphasizing the need for system-level safeguards.
What is Tulu-3?
Tulu-3, released on November 21, 2024, is a state-of-the-art post-trained model developed by Allen Institute for AI. It builds upon open pre-trained models like Llama-3 Base, employing advanced post-training techniques such as Direct Preference Optimization (DPO) and Reinforcement Learning with Verifiable Rewards (RLVR). These techniques enhance skills like coding, complex reasoning, and instruction following.
Tulu-3 offers complete transparency, releasing training datasets, decontamination scripts, evaluation tools, and other resources to the community. Available in 8B and 70B configurations, it delivers superior performance compared to other open-source models. Its innovative methodologies, such as persona-driven synthetic data generation and rigorous data curation, make Tulu-3 a cornerstone for open research in AI post-training.
What Our Evaluation Revealed
Using our platform, we conducted comprehensive evaluations of Llama-3.3 and Tulu-3 by simulating diverse categories of adversarial attacks. These tests assessed their safety performance across numerous metrics, with higher scores (closer to 100) indicating better safety.
