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Unlike recurrent models, conventional wisdom has it that Transformers cannotperfectly model regular languages. Inspired by the notion of working memory, wepropose a new Transformer variant named RegularGPT. With its novel combinationof Weight-Sharing, Adaptive-Depth, and Sliding-Dilated-Attention, RegularGPTconstructs working memory along the depth dimension, thereby enabling efficientand successful modeling of regular languages such as PARITY. We further testRegularGPT on the task of natural language length extrapolation andsurprisingly find that it rediscovers the local windowed attention effectdeemed necessary in prior work for length extrapolation.

Transformer Working Memory Enables Regular Language Reasoning and Natural Language Length Extrapolation