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

MCML Authors

Barbara Plank

Prof. Dr.

Core PI

AI and Computational Linguistics

Abstract

While 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 easy 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 ICLL methods for machine translation with SOTA evaluation tools and manual analysis, explore new directions leveraging otherwise expensive resources for improving ICLL, and test ICLL strategies for 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.

inproceedings BSP+26

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EMNLP 2026

Conference on Empirical Methods in Natural Language Processing. Budapest, Hungary, Oct 24-29, 2026. To be published. Preprint available.

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Authors

N. Bafna • R. S.-E. Shim • B. Plank • D. Yarowsky • H. Sirin

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Links

URL

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Research Area

 B2 | Natural Language Processing