Model4h ago
Children Outlearn LLMs With Far Less Data
Large language models require 100,000 times more words than a human child to achieve linguistic fluency. While GPT-4 and Claude mimic natural conversation, they lack the innate efficiency of human learning. This data gap suggests current architectures are fundamentally inefficient. Researchers must now find ways to mimic human-like sample efficiency to reduce training costs.