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Mila & Universite de Montreal Researchers Introduce the Forgetting Transformer (FoX) to Boost Long-Context Language Modeling without Sacrificing Efficiency

Transformers have revolutionized sequence modeling by introducing an architecture that handles long-range dependencies efficiently without relying on recurrence. Their ability to process input tokens simultaneously, while utilizing self-attention mechanisms, enables them to achieve impressive performance in natural language tasks. However,…