The article doesn't understand large language models. It makes an assumption about how they work and uses that assumption to draw a conclusion. Most likely, a

The article doesn't understand large language models. It makes an assumption about how they work and uses that assumption to draw a conclusion. Most likely, as found in small models [0], large autoregressive transformers understand machine learning algorithms well enough to apply them to their inputs to achieve in-context learning (few-shot prompting) by performing gradient descent within the overall network [1]. This is fundamentally different from static predictors; LLMs are dynamic modelers that learn from context in order to better predict tokens. [0] https://arxiv.org/pdf/2211.15661 https://arxiv.org/pdf/2211.15661 [1] https://arxiv.org/abs/2212.07677 https://arxiv.org/abs/2212.07677