This AI Research Diagnoses Problems in Recurrent Neural Networks RNN-based Language Models and Corrects them to Outperform Transformer-based Models on Long Sequence Tasks

" data-medium-file="https://www.marktechpost.com/wp-content/uploads/2024/11/Screenshot-2024-11-04-at-10.36.35 PM-300x215.png" data-large-file="https://www.marktechpost.com/wp-content/uploads/2024/11/Screenshot-2024-11-04-at-10.36.35 PM-1024x733.png" tabindex="0" role="button"> " data-medium-file="https://www.marktechpost.com/wp-content/uploads/2024/11/Screenshot-2024-11-04-at-10.36.35 PM-300x215.png" data-large-file="https://www.marktechpost.com/wp-content/uploads/2024/11/Screenshot-2024-11-04-at-10.36.35 PM-1024x733.png" tabindex="0" role="button">Recurrent Neural Networks were the trailblazers in natural language processing and set the cornerstone for future advances. RNNs were simple in structure with their contextual memory and constant state size, which promised the capacity to handle long sequence tasks. While theoretically, the design of RNNS pledged to a great future in long context tasks, practically, […]

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