Bridging the data gap between children and large language models

Published on September 1, 2023

Imagine trying to learn how to bake a cake with just a tiny recipe card, while someone else has an entire library of cookbooks at their disposal. That’s the difference between children and large language models (LLMs) when it comes to language learning. LLMs have access to a massive amount of data, while children rely on their pre-existing knowledge and social interactions to bridge the gap. It’s like comparing an encyclopedia to a game of charades. Researchers are fascinated by how LLMs can generate such complex language with so little input, and they’re exploring various theories to explain this phenomenon. Some believe it’s because children bring prior understanding to the table, like already knowing that eggs go in a cake. Others think it’s because children learn through interaction and context, whereas LLMs don’t have that luxury. By understanding these differences, we can unlock the secrets of how language models and children learn, leading to breakthroughs in artificial intelligence and education. Check out the full article for more insights!

Large language models (LLMs) show intriguing emergent behaviors, yet they receive around four or five orders of magnitude more language data than human children. What accounts for this vast difference in sample efficiency? Candidate explanations include children’s pre-existing conceptual knowledge, their use of multimodal grounding, and the interactive, social nature of their input.

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