In this post, we present a replication and extension of an alignment faking model organism:
- Replication: We replicate the alignment faking (AF) paper and release our code.
- Classifier Improvements: We significantly improve the precision and recall of the AF classifier. We release a dataset of ~100 human-labelled examples of AF for which our classifier achieves an AUROC of 0.9 compared to 0.6 from the original classifier.
- Evaluating More Models: We find Llama family models, other open source models, and GPT-4o do not AF in the prompted-only setting when evaluating using our new classifier (other than a single instance with Llama 3 405B).
- Extending SFT Experiments: We run supervised fine-tuning (SFT) experiments on Llama (and GPT4o) and find that AF rate increases with scale. We release the fine-tuned models on Huggingface and scripts.
- Alignment faking on 70B: We find that Llama 70B alignment fakes when both using the system prompt in the [...]
---Outline:(02:43) Method(02:46) Overview of the Alignment Faking Setup(04:22) Our Setup(06:02) Results(06:05) Improving Alignment Faking Classification(10:56) Replication of Prompted Experiments(14:02) Prompted Experiments on More Models(16:35) Extending Supervised Fine-Tuning Experiments to Open-Source Models and GPT-4o(23:13) Next Steps(25:02) Appendix(25:05) Appendix A: Classifying alignment faking(25:17) Criteria in more depth(27:40) False positives example 1 from the old classifier(30:11) False positives example 2 from the old classifier(32:06) False negative example 1 from the old classifier(35:00) False negative example 2 from the old classifier(36:56) Appendix B: Classifier ROC on other models(37:24) Appendix C: User prompt suffix ablation(40:24) Appendix D: Longer training of baseline docs--- First published: April 8th, 2025 Source: https://www.lesswrong.com/posts/Fr4QsQT52RFKHvCAH/alignment-faking-revisited-improved-classifiers-and-open) --- Narrated by TYPE III AUDIO). ---Images from the article:))
