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Continued Effectiveness of Scaling up Training in NLP
Historically, a traditional view in natural language processing suggested that performance gains would eventually disappear as model training scales up. However, contemporary findings indicate that on a sufficiently large scale, expanding the training process remains a highly potent approach for producing more capable Large Language Models. Notably, even after processing trillions of tokens, both proprietary and open-source models continue to exhibit performance improvements when trained with additional data.
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Foundations of Large Language Models
Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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A research team is training a large language model and has a fixed, non-negotiable computational budget. Their goal is to achieve the lowest possible final loss. Based on the established principles that govern the relationship between computation, model size, data size, and performance, which of the following strategies represents the most efficient use of their budget?
Evaluating an LLM Training Strategy
Analyzing Deviations from LLM Scaling Behavior
Continued Effectiveness of Scaling up Training in NLP
Power-Law Curve of Performance Scaling
Scaling Laws Across LLM Development Stages