Children, but not language models, show accelerating returns in word learning

2026-08-17

AI Summary

研究探讨了儿童词汇学习加速的现象，并指出这种加速在语言模型中未出现，提出儿童更高效利用学习输入的假设。

Abstract

Children learn hundreds of words over the first years of their lives, in a process that begins slowly but quickly picks up speed. Prior models describe vocabulary growth as evidence accumulation over time. Here we show that the process is best characterized as accelerating accumulation: children learn more from each additional unit of linguistic experience than they did from the one before. In contrast to children, language models -- even those trained on child-directed speech -- do not accelerate. Instead, they show constant proportional returns on new data, consistent with scaling laws. Children learn using many orders of magnitude less training data than language models; their increasingly efficient use of their learning input is a candidate explanation.

Problem

• accelerating returns
• word learning
• language models
• children
• vocabulary growth

Innovation

• accelerating returns
• vocabulary growth
• language models
• scaling laws