Understanding priors in language generation models

我研究的目标是，通过理解语言的底层语法和单词之间的语义关系，使算法生成文本成为可能。重点是探索不同的神经网络架构。一个案例研究是机器训练中的幻觉。在机器翻译中，幻觉是“语义完全不正确，语法也可行”的代。深入了解这一现象很重要，这不仅是因为幻觉会损害商业系统用户的信心。NMT中的幻觉是高度病理性输出的令人惊讶的明确案例，任何关于它们如何或为什么发生的见解都将有助于回答为什么变形金刚会产生语言（与人类一致）的问题？

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)?