title: Towards eliciting latent knowledge from LLMs with mechanistic
    interpretability
summary: 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.
paper_authors._id: 682d72b0d57ba1e4d132148d
paper_authors.name: Bartosz Cywiński
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-21T08:39:48.008Z
paper_authors._id: 6422f416a73327caad9d1d86
paper_authors.avatarUrl: /avatars/aa3639277cd1732504402fc64a57eff8.svg
paper_authors.fullname: Bartosz Cywiński
paper_authors.type: user
paper_authors.user: bcywinski
paper_authors._id: 682d72b0d57ba1e4d132148e
paper_authors.name: Emil Ryd
paper_authors._id: 682d72b0d57ba1e4d132148f
paper_authors.name: Senthooran Rajamanoharan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-21T10:37:33.691Z
paper_authors._id: 6477122c99a5ce743ccf2f55
paper_authors.avatarUrl: /avatars/dee617374fc609e07eba3bcb2cd16810.svg
paper_authors.fullname: Senthooran Rajamanoharan
paper_authors.type: user
paper_authors.user: srdm
paper_authors._id: 682d72b0d57ba1e4d1321490
paper_authors.name: Neel Nanda
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-21T10:37:18.664Z
paper_authors._id: 62669380c8bc5cf80ca97350
paper_authors.avatarUrl: /avatars/6d5cd2261163308b82341c1ce28984d1.svg
paper_authors.fullname: Neel Nanda
paper_authors.type: user
paper_authors.user: NeelNanda
paper_title: Towards eliciting latent knowledge from LLMs with mechanistic
    interpretability
paper_summary: 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.
paper_ai_summary: Methods using logit lens and sparse autoencoders effectively uncover hidden knowledge in language models trained to keep secrets.
paper_ai_keywords: logit lens sparse autoencoders mechanistic interpretability Taboo model