title: ICL CIPHERS: Quantifying "Learning'' in In-Context Learning via
    Substitution Ciphers
summary: Recent works have suggested that In-Context Learning (ICL) operates in dual
  modes, i.e. task retrieval (remember learned patterns from pre-training) and
  task learning (inference-time ``learning'' from demonstrations). However,
  disentangling these the two modes remains a challenging goal. We introduce ICL
  CIPHERS, a class of task reformulations based on substitution ciphers borrowed
  from classic cryptography. In this approach, a subset of tokens in the
  in-context inputs are substituted with other (irrelevant) tokens, rendering
  English sentences less comprehensible to human eye. However, by design, there
  is a latent, fixed pattern to this substitution, making it reversible. This
  bijective (reversible) cipher ensures that the task remains a well-defined task
  in some abstract sense, despite the transformations. It is a curious question
  if LLMs can solve ICL CIPHERS with a BIJECTIVE mapping, which requires
  deciphering the latent cipher. We show that LLMs are better at solving ICL
  CIPHERS with BIJECTIVE mappings than the NON-BIJECTIVE (irreversible) baseline,
  providing a novel approach to quantify ``learning'' in ICL. While this gap is
  small, it is consistent across the board on four datasets and six models.
  Finally, we examine LLMs' internal representations and identify evidence in
  their ability to decode the ciphered inputs.
paper_authors._id: 6810767a0f244cf14e5a3060
paper_authors.name: Zhouxiang Fang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-29T08:01:43.304Z
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paper_authors.fullname: Zhouxiang Fang
paper_authors.type: user
paper_authors.user: FocusV857
paper_authors._id: 6810767a0f244cf14e5a3061
paper_authors.name: Aayush Mishra
paper_authors._id: 6810767a0f244cf14e5a3062
paper_authors.name: Muhan Gao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-29T12:28:30.870Z
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paper_authors.fullname: Muhan Gao
paper_authors.type: user
paper_authors.user: MuhanGao
paper_authors._id: 6810767a0f244cf14e5a3063
paper_authors.name: Anqi Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-29T12:28:36.732Z
paper_authors._id: 67c90731746bbcdbdfa0ec3e
paper_authors.avatarUrl: /avatars/d62924f319b532399b2f1fc60d7df04c.svg
paper_authors.fullname: Anqi Liu
paper_authors.type: user
paper_authors.user: AnqLiu
paper_authors._id: 6810767a0f244cf14e5a3064
paper_authors.name: Daniel Khashabi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-29T12:28:42.225Z
paper_authors._id: 5f6540c65e78cc6b0ed3199d
paper_authors.avatarUrl: /avatars/0280d4df417855965a0964d22766c012.svg
paper_authors.fullname: Daniel Khashabi
paper_authors.type: user
paper_authors.user: danyaljj
paper_title: ICL CIPHERS: Quantifying "Learning'' in In-Context Learning via
    Substitution Ciphers
paper_summary: Recent works have suggested that In-Context Learning (ICL) operates in dual
  modes, i.e. task retrieval (remember learned patterns from pre-training) and
  task learning (inference-time ``learning'' from demonstrations). However,
  disentangling these the two modes remains a challenging goal. We introduce ICL
  CIPHERS, a class of task reformulations based on substitution ciphers borrowed
  from classic cryptography. In this approach, a subset of tokens in the
  in-context inputs are substituted with other (irrelevant) tokens, rendering
  English sentences less comprehensible to human eye. However, by design, there
  is a latent, fixed pattern to this substitution, making it reversible. This
  bijective (reversible) cipher ensures that the task remains a well-defined task
  in some abstract sense, despite the transformations. It is a curious question
  if LLMs can solve ICL CIPHERS with a BIJECTIVE mapping, which requires
  deciphering the latent cipher. We show that LLMs are better at solving ICL
  CIPHERS with BIJECTIVE mappings than the NON-BIJECTIVE (irreversible) baseline,
  providing a novel approach to quantify ``learning'' in ICL. While this gap is
  small, it is consistent across the board on four datasets and six models.
  Finally, we examine LLMs' internal representations and identify evidence in
  their ability to decode the ciphered inputs.
paper_ai_summary: ICL CIPHERS, a novel task reformulation using bijective substitution ciphers, helps quantify learning in In-Context Learning by challenging LLMs to decipher latent patterns, showing better performance and internal evidence of decryption compared to non-bijective mappings.
paper_ai_keywords: In-Context Learning task retrieval task learning substitution ciphers bijective mapping irreducible mapping ICL CIPHERS internal representations LLMs