Jan Bauer

Work done for our MATS 10.0 Sprint project - mentored by Neel Nanda and Adam Karvonen Huggingface, Github TL;DR: We have improved the original Activation Oracle (AO) training regime by training on on-policy rollouts, improving the conversational dataset, feeding more layers (following the approach by Niclas Luick) and making a small change to the injection formula. We also open source our evals, which we believe are currently the most comprehensive evaluation of AO quality called AObench. The capability improvements are marginal, but quality of life improvements are quite substantial. If you want to play around with the new AOs, we recommend you use this one, if you want to play with our new Activation Oracles live, we will host them for a week on ao.celeste.computer. Alternatively, you can self-host our web interface. Activation Oracles (AOs) by Karvonen et al. are fine-tuned LLMs that can receive the original target LLM’s activations as input and answer natural language questions about them. However, they are plagued by various issues, which limit their usefulness as an off-the-shelf tool for interpretability research. For our MATS Sprint, we set out to work on these issues. Issues with current Activation Oracles In Current activation oracles are hard to use, Arya Jakkli demonstrates scenarios where AOs are hard to work with. We focus on addressing two of the issues pointed out: * Hallucinations: The AO will output false information. * Vagueness: The AO output will be generic (therefore unfalsifiable) and will not answer the user’s question. In addition, they are difficult to evaluate because of the problem of text inversion: the model infers the surrounding text and answers based on that, just as any black box oracle (i.e. a method that only receives text) could, rather than extracting specific info from activations. As part of our evaluations, we focus on some of his specific tasks, you can find details on our