Building Better Activation Oracles

Abstract:Activation Oracles (AOs) are promising methods for interpreting residual stream activations. However, current AOs face important issues, such as hallucinations and vagueness. Additionally, text-inversion confounds make them hard to evaluate. To this end, we improve the Activation Oracle (AO) training regime in four ways: training on on-policy rollouts, improving the conversational dataset, feeding more layers and an improvement to the injection formula. The capability improvements are marginal, but quality of life improvements are quite substantial. In addition, we open source the first comprehensive evaluation suite for AO quality, which we call AObench. Overall, we hope that our work sets a foundation that helps improve AOs and other models in the paradigm of scalable, end-to-end interpretability.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2606.02609 [cs.LG]

(or arXiv:2606.02609v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2606.02609 Focus to learn more arXiv-issued DOI via DataCite

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