The Representative Capacity of Transformer Language Models LMs with n-gram Language Models LMs: Capturing the Parallelizable Nature of n-gram LMs

Neural language models (LMs) have become popular due to their extensive theoretical work mostly focusing on representational capacity. An earlier study of representational capacity using Boolean sequential models helps in a proper understanding of its lower and upper bound and the potential of the transformer architecture. LMs have become the backbone of many NLP tasks, […]

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