I don't disagree with you, these models may be underfitted, but overfitting is not explicitly defined by val vs. training loss, but rather how closely its outpu

I don't disagree with you, these models may be underfitted, but overfitting is not explicitly defined by val vs. training loss, but rather how closely its output matches training data. If you trained a MLP model where the number of parameters exceeded the data, it would be able to memorize the data and return a zero loss on training data. The larger the models are, the greater chance it memorizes the data, rather than the latent variables or distribution of the data. Early LLMs, GPT2 (circa 2019) for example was definitely overfitting. I would frequently copy and paste output and find a reddit comment with the exact words.