title: Self-Recognition in Language Models
summary: A rapidly growing number of applications rely on a small set of closed-source
  language models (LMs). This dependency might introduce novel security risks if
  LMs develop self-recognition capabilities. Inspired by human identity
  verification methods, we propose a novel approach for assessing
  self-recognition in LMs using model-generated "security questions". Our test
  can be externally administered to keep track of frontier models as it does not
  require access to internal model parameters or output probabilities. We use our
  test to examine self-recognition in ten of the most capable open- and
  closed-source LMs currently publicly available. Our extensive experiments found
  no empirical evidence of general or consistent self-recognition in any examined
  LM. Instead, our results suggest that given a set of alternatives, LMs seek to
  pick the "best" answer, regardless of its origin. Moreover, we find indications
  that preferences about which models produce the best answers are consistent
  across LMs. We additionally uncover novel insights on position bias
  considerations for LMs in multiple-choice settings.
paper_authors._id: 66910b82f84c9502c4422cb0
paper_authors.name: Tim R. Davidson
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-07-12T11:40:19.097Z
paper_authors._id: 65c9e65bd6bed59d801a5eb3
paper_authors.avatarUrl: /avatars/431bc46350cbbe996bb058e03ccc2fd0.svg
paper_authors.fullname: Tim Davidson
paper_authors.type: user
paper_authors.user: trdavidson
paper_authors._id: 66910b82f84c9502c4422cb1
paper_authors.name: Viacheslav Surkov
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-12T12:02:17.091Z
paper_authors._id: 656f19b7d6f611cd4d61ec8c
paper_authors.avatarUrl: /avatars/489fc5dcca17bf18ece4091783c14fa9.svg
paper_authors.fullname: Viacheslav Surkov
paper_authors.type: user
paper_authors.user: surokpro
paper_authors._id: 66910b82f84c9502c4422cb2
paper_authors.name: Veniamin Veselovsky
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-12T12:02:23.224Z
paper_authors._id: 64f456db8db247808c1fc0cc
paper_authors.avatarUrl: /avatars/2032dccca99ac0dac6f69eeecfc90087.svg
paper_authors.fullname: Veniamin Veselovsky
paper_authors.type: user
paper_authors.user: vminvsky
paper_authors._id: 66910b82f84c9502c4422cb3
paper_authors.name: Giuseppe Russo
paper_authors._id: 66910b82f84c9502c4422cb4
paper_authors.name: Robert West
paper_authors._id: 66910b82f84c9502c4422cb5
paper_authors.name: Caglar Gulcehre
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-12T12:02:49.619Z
paper_authors._id: 64d28085db0b63b977a6c3f8
paper_authors.avatarUrl: /avatars/994cc3ff7c18650fe2535e473d56b240.svg
paper_authors.fullname: Caglar Gulcehre
paper_authors.type: user
paper_authors.user: caglarg
paper_title: Self-Recognition in Language Models
paper_summary: A rapidly growing number of applications rely on a small set of closed-source
  language models (LMs). This dependency might introduce novel security risks if
  LMs develop self-recognition capabilities. Inspired by human identity
  verification methods, we propose a novel approach for assessing
  self-recognition in LMs using model-generated "security questions". Our test
  can be externally administered to keep track of frontier models as it does not
  require access to internal model parameters or output probabilities. We use our
  test to examine self-recognition in ten of the most capable open- and
  closed-source LMs currently publicly available. Our extensive experiments found
  no empirical evidence of general or consistent self-recognition in any examined
  LM. Instead, our results suggest that given a set of alternatives, LMs seek to
  pick the "best" answer, regardless of its origin. Moreover, we find indications
  that preferences about which models produce the best answers are consistent
  across LMs. We additionally uncover novel insights on position bias
  considerations for LMs in multiple-choice settings.
paper_ai_keywords: self-recognition security questions model-generated position bias