Right now yes! Most of the papers published recently (like Chinchilla, GPT, etc.) show a scaling law on the number of data wrt the number of params in a model. If you want a no-brain training with little preprocessing, bigger models are mostly better. However, if you have sufficient data, then the number of params needed may be mitigated. However, I feel like the number of parameters decreases really slow when the data size grows. So yeah, we still somehow need larger model (of course, this also depends on the scenario where you apply LLM, for example, you don't really need that big of a model for an ecom app)