Ed and Anna are co-first authors on this work.

• Recent work on Emergent Misalignment (EM) found that fine-tuning LLMs on narrowly harmful datasets can cause them to become broadly misaligned.
• We find a linear direction for misalignment in emergently misaligned models. We can add this to the chat model to misalign it, and we can ablate it from the EM model to re-align it.
• This direction is convergent: the direction derived from one fine-tune can also be used to ablate misalignment from others, trained on different datasets and with higher dimensional fine-tuning.
• As detailed in our parallel post, emergent misalignment can be induced with rank-1 LoRA adapters.  Here, we treat these adapters as a scalar value which multiplies a steering vector, and show how this is valuable for interpretability,
• Through probing and steering experiments, we show that some LoRA adapters specialise for the narrow dataset context, while others are responsible [...]

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Outline:

(00:17) TL;DR

(01:37) Introduction

(04:00) Manipulating Misalignment Directions

(04:51) Steering for Misalignment

(05:32) Ablating Misalignment

(07:44) Comparing to a Single Rank-1 Adapter Fine-tune

(09:44) Steering for Different 'Modes' of Misalignment

(11:35) Interpreting LoRA Adapters

(12:10) Probing LoRA Scalars

(14:47) Steering LoRA Adapters

(16:29) Future Work

(17:45) Contributions Statement

(18:08) Acknowledgments

The original text contained 6 footnotes which were omitted from this narration.

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First published:

Source:

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Narrated by TYPE III AUDIO.

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