COLLAGE: A New Machine Learning Approach to Deal with Floating-Point Errors in Low-Precision to Make LLM Training Accurate and Efficient

Large language models (LLMs) have revolutionized natural language processing, enabling groundbreaking advancements in various applications such as machine translation, question-answering, and text generation. However, the training of these models poses significant challenges, including high resource requirements and long training times due to the complexity of the computations involved.  Previous research has explored techniques like loss-scaling […]

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