I'm a researcher in ML and LLMs absolutely fall under ML. Learning in the term "Machine Learning" just means fitting the parameters of a model, hence just an optimization problem. In the case of an LLM this means fitting parameters of the transformer.

A model doesn't have to be intelligent to fall under the umbrella of ML. Linear least squares is considered ML; in fact, it's probably the first thing you'll do if you take an ML course at a university. Decision trees, nearest neighbor classifiers, and linear models all are machine learning models, despite the fact that nobody would consider them to be intelligent.

LLMs are a type of machine learning. Input is broken into tokens, which are then fed through a type of neural network called a transformer model.

The models are trained with a process known as deep learning, which involves the probabilistic analysis of unstructured data, which eventually enables the model to recognize distinctions between pieces of content.

That's like textbook machine learning. What you said about interpreting sentiment isn't wrong, but it does so with machine learning algorithms.

LLMs are deep learning models that were developed off of multi-head attention/transformer layers. They are absolutely Machine Learning as they use a blend of supervised and unsupervised training (plus some reinforcement learning with some recent developments like DeepSeek).