ICL CIPHERS: Quantifying ``Learning'' in In-Context Learning via Substitution Ciphers

ACL ARR 2025 May Submission2342 Authors

Abstract: 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, our interpretability analysis shows evidence that LLMs can internally decode ciphered inputs.

Paper Type: Long

Research Area: Machine Learning for NLP

Research Area Keywords: few-shot learning

Contribution Types: Model analysis & interpretability, NLP engineering experiment

Languages Studied: English

Submission Number: 2342