The Nvidia team/blog cannot be clearer about the challenges of AI & NLP: “Recent work has demonstrated that larger language models dramatically advance the state of the art in natural language processing (NLP) applications such as question-answering, dialog systems, summarization, and article completion. However, during training, large models do not fit in the available memory of a single accelerator, requiring model parallelism to split the parameters across multiple accelerators.” See details at "State-of-the-Art Language Modeling Using Megatron on the NVIDIA A100 GPU" https://devblogs.nvidia.com/language-modeling-using-megatron-a100-gpu
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One of the flaws of usual training data generation is that, when you ask somebody to manually create training data for you, they will make an effort to write these sentences correctly, following the spelling and punctuation norms of your language. Even if some errors appear, they will be minimal, because they are trying to do things right —this is, to provide “orthographically right” sentences.
All Machine Learning (ML) engines that work with text can benefit from a solid linguistic background. If they are working in a multilingual environment, the need of a good lexicon (with forms, lemmas and attributes) is overwhelming. Even so, basic features such as Word Embeddings hugely improve when enriched with linguistic knowledge, and if this is not usually applied, is because of a lack of linguists working for ML companies.