Confirm Labs

Confirm Labs is a research group run by Michael Sklar and Ben Thompson. In April 2023, we transitioned into AI safety from our past work on statistical theory/software to speed up the FDA.

We have two ongoing projects:

1. Adversarial attacks: We believe that developing better white-box adversarial techniques can help with (a) evaluating model capabilities via red-teaming (b) model interpretability (c) providing data and feedback for safety-training pipelines.

   Recently, we have built methods for powerful and fluent adversarial attacks. This work is currently being compiled into a paper to be released in May 2024. Earlier this year, we published “Fluent Dreaming for Language Models” which combines whitebox optimization with interpretability. We also won a division of the NeurIPS 2023 Trojan Detection Competition.

2. Pretraining AI editor architectures: We believe AI inspection of AI internals could become a useful component of AI interpretability and oversight. Inspired by the success of the pre-training paradigm in language models, we are designing models that are trained to understand the inner workings of a target model. In particular, we are building editor architectures that take as inputs the activations of a frozen target model as well as language-based editing instructions, and as their output will “puppet” the activation stream of the target model to achieve desired results. Fine-tuning the resulting model for interpretability tasks could result in powerful tools for interpretability or oversight.

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