Understanding priors in language generation models

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The objective of my research is what makes it possible for algorithms to generate text, both by understanding the underlying grammar of language and the semantic relationship between words. The focus is on the exploration of different neural network architectures.One case study is that of hallucinations in machine trainslation.In machine translation, hallucinations are generations that are "completely semantically incorrect and also grammatically viable". It is important to understand in depth this phenomenon, not only because hallucinations can harm the confidence of users of commercial systems. Hallucinations in NMT are surprisingly well-defined cases of highly pathological outputs and any insight on how or why they happen will contribute towards answering the question why do transformers generate language (that is coherent to humans)?