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We explored the role of SAE errors in two different contexts for Gemma-2 and Gemma Scope SAEs: sparse feature circuits (subject-verb-agreement-across-relative clause) and linear probing. Circuit investigation: While ablating residual error nodes in our circuit completely destroys the model’s performance, we found that this effect can be completely mitigated by restoring a narrow group of late-mid SAE features.
This is a project submission post for the AI Safety Fundamentals course from BlueDot Impact. Therefore, some of its sections are intended to be beginner-friendly and overly verbose for familiar readers (mainly the Introduction section) and may freely be skipped.
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