Tag
An opinion piece argues that AI models acquire dangerous knowledge from training data, and that companies like Anthropic and OpenAI rely on easily breakable refusal filters instead of truly removing harmful capabilities, prioritizing speed over safety.
The paper argues that unlearning in LLMs should be goal-dependent, proposing a cosine-based meta-learned variant of RMU for dangerous knowledge and a multi-layer objective with probe directions for toxicity, achieving strong results across four 7-8B models.