Learn why smaller LLMs can struggle with one-shot and few-shot inference, and how larger LLMs can overcome this challenge with prompt engineering.
“Smaller LLMs can struggle with one-shot and few-shot inference:” Is this true or false?
The correct answer is A. True. Smaller LLMs can struggle with one-shot and few-shot inference because they have less capacity to learn from a single or a few examples. Larger LLMs, on the other hand, can leverage their massive parameters and pre-trained knowledge to generalize better to new tasks or domains with minimal supervision.
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