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Performance Scaling in GPT-3
Empirical evaluations of GPT-3 demonstrate that its performance across various benchmarks consistently improves as its parameter count increases. Within the in-context learning paradigm, the model's few-shot performance exhibits the most rapid and pronounced gains as the model size scales up. Furthermore, GPT-3's performance (measured by cross-entropy validation loss) follows a predictable power-law trend with the amount of compute used for training. This empirical scaling behavior continues established power-law trends for an additional two orders of magnitude with only small deviations.
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Performance Scaling in GPT-3
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Performance Scaling in GPT-3