The primary problem that large language models solved is small sample learning, right?
LLMs are not the first mechanism to solve the issue of small-sample learning. Given that LLMs are trained to generate novel responses in accordance with the huge complementary text corpora, They are good at getting context, generating human interpretable text, and text-related NLP tasks. They are capable of, but not intended for, few-shot or zero-shot learning.
Tong Guo
Large Language Models (LLMs) have effectively addressed various challenges, and small sample learning is one of them. They address small sample learning by utilizing their pre-trained knowledge and refining their understanding with domain-specific data.
It's important to note that while LLMs handle small sample learning well, they can still benefit from more data. Researchers are continually exploring ways to further improve their capabilities.
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