What Formal Languages Can Transformers Express? A Survey

November 01, 2023 ยท Declared Dead ยท ๐Ÿ› Transactions of the Association for Computational Linguistics

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Authors Lena Strobl, William Merrill, Gail Weiss, David Chiang, Dana Angluin arXiv ID 2311.00208 Category cs.LG: Machine Learning Cross-listed cs.CL, cs.FL, cs.LO Citations 107 Venue Transactions of the Association for Computational Linguistics Last Checked 4 months ago
Abstract
As transformers have gained prominence in natural language processing, some researchers have investigated theoretically what problems they can and cannot solve, by treating problems as formal languages. Exploring such questions can help clarify the power of transformers relative to other models of computation, their fundamental capabilities and limits, and the impact of architectural choices. Work in this subarea has made considerable progress in recent years. Here, we undertake a comprehensive survey of this work, documenting the diverse assumptions that underlie different results and providing a unified framework for harmonizing seemingly contradictory findings.
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