  Double-blind peer review is considered a pillar of academic research because
it is perceived to ensure a fair, unbiased, and fact-centered scientific
discussion. Yet, experienced researchers can often correctly guess from which
research group an anonymous submission originates, biasing the peer-review
process. In this work, we present a transformer-based, neural-network
architecture that only uses the text content and the author names in the
bibliography to attribute an anonymous manuscript to an author. To train and
evaluate our method, we created the largest authorship identification dataset
to date. It leverages all research papers publicly available on arXiv amounting
to over 2 million manuscripts. In arXiv-subsets with up to 2,000 different
authors, our method achieves an unprecedented authorship attribution accuracy,
where up to 73% of papers are attributed correctly. We present a scaling
analysis to highlight the applicability of the proposed method to even larger
datasets when sufficient compute capabilities are more widely available to the
academic community. Furthermore, we analyze the attribution accuracy in
settings where the goal is to identify all authors of an anonymous manuscript.
Thanks to our method, we are not only able to predict the author of an
anonymous work, but we also provide empirical evidence of the key aspects that
make a paper attributable. We have open-sourced the necessary tools to
reproduce our experiments.
