Towards eliciting latent knowledge from LLMs with mechanistic
  interpretability
Authors: name: Bartosz Cywiński, name: Emil Ryd, name: Senthooran Rajamanoharan, name: Neel Nanda

As language models become more powerful and sophisticated, it is crucial that
they remain trustworthy and reliable. There is concerning preliminary evidence
that models may attempt to deceive or keep secrets from their operators. To
explore the ability of current techniques to elicit such hidden knowledge, we
train a Taboo model: a language model that describes a specific secret word
without explicitly stating it. Importantly, the secret word is not presented to
the model in its training data or prompt. We then investigate methods to
uncover this secret. First, we evaluate non-interpretability (black-box)
approaches. Subsequently, we develop largely automated strategies based on
mechanistic interpretability techniques, including logit lens and sparse
autoencoders. Evaluation shows that both approaches are effective in eliciting
the secret word in our proof-of-concept setting. Our findings highlight the
promise of these approaches for eliciting hidden knowledge and suggest several
promising avenues for future work, including testing and refining these methods
on more complex model organisms. This work aims to be a step towards addressing
the crucial problem of eliciting secret knowledge from language models, thereby
contributing to their safe and reliable deployment.