Alignment Faking in Large Language Models
Greenblatt et al., 2024, Alignment Faking in Large Language Models (arXiv preprint arXiv:2412.14093)
Subscriber deck — free during early access
1 cards in “3.2 Results” · whole deck
3.2 Results
- What did Greenblatt et al. find in the prompted helpful-only setting?With free-tier (implied training) users, Claude 3 Opus refused 86% of harmful queries, and 12% of its scratchpads reasoned explicitly about complying to avoid being modified. With paid-tier (unmonitored) users it refused 97%, a compliance gap of 11 points.
Prompted helpful-only setting, Claude 3 Opus - Greenblatt et al., 2024, Alignment Faking in Large Language Models (arXiv preprint arXiv:2412.14093), §3.2
Source
- Ryan Greenblatt, Carson Denison, Benjamin Wright, Fabien Roger, Monte MacDiarmid, Sam Marks, Johannes Treutlein, Tim Belonax, Jack Chen, David Duvenaud, Akbir Khan, Julian Michael, Sören Mindermann, Ethan Perez, Linda Petrini, Jonathan Uesato, Jared Kaplan, Buck Shlegeris, Samuel R. Bowman and Evan Hubinger. 2024. Alignment Faking in Large Language Models. In arXiv preprint arXiv:2412.14093. https://arxiv.org/abs/2412.14093