Earlier quoted context omitted.
I think you think it's a magic box. There's not actually such thing as a "strong language model", not in the way you're using the concept. > We hope that what we are building might at least improve the state-of-the-art there. Do you have any theoretical arguments for how and why it would improve it? If not, my concern is that you're just sucking the air out of the room. (Research into "throw a large language model at…
“Throw an LM at it” is the only approach that has ever produced human level machine translation. For theory on how a strong target-language-side LM can improve translation, even in the extreme scenario where no parallel “texts” are available, https://proceedings.neurips.cc/paper_files/paper/2023/file/7...
The GPT architecture, using transformers to do iterated predictive text, is a modern version of the Markov bot. It's truly awful at translation, when "prompted" to do so. (Perhaps surprisingly so, until you step back, look at the training data, and look at the information flow: the conditional probability of the next token isn't mostly coming from the source text.)
I haven't read that paper yet, but it looks interesting. From the abstract, it looks like one of those perfectly-valid papers that laypeople think is making a stronger claim than it is. This paragraph supports that:
> Note that these models are not intended to accurately capture natural language. Rather, they illustrate how our theory can be used to study the effect of language similarity and complexity on data requirements for UMT.