  In this work, we tackle the problem of structured text generation,
specifically academic paper generation in $\LaTeX{}$, inspired by the
surprisingly good results of basic character-level language models. Our
motivation is using more recent and advanced methods of language modeling on a
more complex dataset of $\LaTeX{}$ source files to generate realistic academic
papers. Our first contribution is preparing a dataset with $\LaTeX{}$ source
files on recent open-source computer vision papers. Our second contribution is
experimenting with recent methods of language modeling and text generation such
as Transformer and Transformer-XL to generate consistent $\LaTeX{}$ code. We
report cross-entropy and bits-per-character (BPC) results of the trained
models, and we also discuss interesting points on some examples of the
generated $\LaTeX{}$ code.
