  Eliciting Latent Knowledge (ELK) aims to find patterns in a capable neural
network's activations that robustly track the true state of the world,
especially in hard-to-verify cases where the model's output is untrusted. To
further ELK research, we introduce 12 datasets and a corresponding suite of
"quirky" language models (LMs) that are finetuned to make systematic errors
when answering questions if and only if the keyword "Bob" is present in the
prompt. We find that, especially in middle layers, linear probes usually report
an LM's knowledge independently of what the LM outputs, enabling us to elicit
the correct answer despite the model's untruthful output. The best probing
method (logistic regression on contrast pairs) recovers 89% of the gap in AUROC
between truthful and untruthful contexts, and 75% for questions harder than
those used to train the probe. We also find that a mechanistic anomaly
detection approach can flag untruthful behavior with 0.95 AUROC. Our results
show promise for eliciting reliable knowledge from capable but untrusted
models, and facilitates future research empirically investigating ELK methods.
