A few days ago, Gray Swan published code and models for their recent “circuit breakers” method for language models.[1]1

The circuit breakers method defends against jailbreaks by training the model to erase “bad” internal representations. We are very excited about data-efficient defensive methods like this, especially those which use interpretability concepts or tools.

At the link, we briefly investigate three topics:

1. Increased refusal rates on harmless prompts: Do circuit breakers really maintain language model utility? Most defensive methods come with a cost. We check the model's effectiveness on harmless prompts, and find that the refusal rate increases from 4% to 38.5% on or-bench-80k.
2. Moderate vulnerability to different token-forcing sequences: How specialized is the circuit breaker defense to the specific adversarial attacks they studied? All the attack methods evaluated in the circuit breaker paper rely on a “token-forcing” optimization objective which maximizes the likelihood of [...]

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First published:

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Narrated by TYPE III AUDIO.