• “Alignment Faking Revisited: Improved Classifiers and Open Source Extensions” by John Hughes, abhayesian, Akbir Khan, Fabien Roger

  • Apr 9 2025
  • Length: 41 mins
  • Podcast

“Alignment Faking Revisited: Improved Classifiers and Open Source Extensions” by John Hughes, abhayesian, Akbir Khan, Fabien Roger

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  • Summary

  • 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 [...]
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    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

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    First published:
    April 8th, 2025

    Source:
    https://www.lesswrong.com/posts/Fr4QsQT52RFKHvCAH/alignment-faking-revisited-improved-classifiers-and-open

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    Narrated by TYPE III AUDIO.

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