ICL CIPHERS: Quantifying "Learning'' in In-Context Learning via
  Substitution Ciphers
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.
6810767a0f244cf14e5a3060
Zhouxiang Fang
claimed_verified
2025-04-29T08:01:43.304Z
6675c9305eaa9dd299dcdca0
/avatars/504a259033605e489809c8f202538d75.svg
Zhouxiang Fang
user
FocusV857
6810767a0f244cf14e5a3061
Aayush Mishra
6810767a0f244cf14e5a3062
Muhan Gao
admin_assigned
2025-04-29T12:28:30.870Z
667acc4d536de14d3673f908
https://cdn-avatars.huggingface.co/v1/production/uploads/667acc4d536de14d3673f908/9a6DhceyckCHXlO0pBBme.jpeg
Muhan Gao
user
MuhanGao
6810767a0f244cf14e5a3063
Anqi Liu
admin_assigned
2025-04-29T12:28:36.732Z
67c90731746bbcdbdfa0ec3e
/avatars/d62924f319b532399b2f1fc60d7df04c.svg
Anqi Liu
user
AnqLiu
6810767a0f244cf14e5a3064
Daniel Khashabi
admin_assigned
2025-04-29T12:28:42.225Z
5f6540c65e78cc6b0ed3199d
/avatars/0280d4df417855965a0964d22766c012.svg
Daniel Khashabi
user
danyaljj
ICL CIPHERS: Quantifying "Learning'' in In-Context Learning via
  Substitution Ciphers
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.
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.
In-Context Learning task retrieval task learning substitution ciphers bijective mapping irreducible mapping ICL CIPHERS internal representations LLMs