Rashid: A Cipher-Based Framework for Exploring In-Context Language Learning

arXiv cs.CL / 3/25/2026

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Key Points

The paper introduces “Rashid,” a cipher-based framework to study in-context language learning (ICLL) on languages that are truly unseen by reversing-ciphering high-resource languages to create new, controlled language variants.
It argues that this approach addresses key limitations in ICLL for unseen languages—namely limited NLP tooling/data, constrained expertise, and difficulty running cheap large-scale experiments.
Using Rashid, the authors benchmark and analyze state-of-the-art ICLL methods with both SOTA evaluation tools and manual analysis to assess current performance more broadly.
They explore which otherwise expensive resources can improve ICLL and test ICLL strategies on rich downstream tasks beyond machine translation.
The work aims to unlock previously impossible experiments and provides actionable insights and directions for future ICLL research.

Abstract

Where there is growing interest in in-context language learning (ICLL) for unseen languages with large language models, such languages usually suffer from the lack of NLP tools, data resources, and researcher expertise. This means that progress is difficult to assess, the field does not allow for cheap large-scale experimentation, and findings on ICLL are often limited to very few languages and tasks. In light of such limitations, we introduce a framework (Rashid), for studying ICLL wherein we reversibly cipher high-resource languages (HRLs) to construct truly unseen languages with access to a wide range of resources available for HRLs, unlocking previously impossible exploration of ICLL phenomena. We use our framework to assess current methods in the field with SOTA evaluation tools and manual analysis, explore the utility of potentially expensive resources in improving ICLL, and test ICLL strategies on rich downstream tasks beyond machine translation. These lines of exploration showcase the possibilities enabled by our framework, as well as providing actionable insights regarding current performance and future directions in ICLL.