This AI Paper by Microsoft and Tsinghua University Introduces YOCO: A Decoder-Decoder Architectures for Language Models | Windows

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Language modeling, a core component of machine learning, involves predicting the likelihood of a sequence of words. This field primarily enhances machine understanding and generation of human language, serving as a backbone for various applications such as text summarization, translation, and auto-completion systems. Efficient language modeling faces significant hurdles, particularly with large models. The main challenge is the computational and memory overhead associated with processing and storing extensive data sequences, which hampers scalability and real-time processing capabilities.

Existing research in language modeling prominently features the Transformer architecture, known for its self-attention mechanism that effectively processes word sequences regardless of distance. Distinguished adaptations include the decoder-only Transformer, optimizing text generation processes in models like OpenAI’s GPT series. Innovations like Sparse Transformers have also emerged, reducing computational demands by limiting interactions between distant sequence elements. Moreover, hybrid models such as BERT and T5 combine various architectural strengths, enhancing…

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