Emergence of Complex Skills in Language Models

The study published on arXiv proposes a theoretical framework to understand how complex language skills emerge in large language models. Using statistical and graph-based analyses, the research explores the scaling laws of model training, indicating that as language models increase in size, they gain abilities akin to "slingshot generalization," which allows them to perform complex tasks that weren't explicitly trained for. This emergent behavior suggests models can combine simple skills in novel ways to handle more intricate tasks effectively.