  In this paper, we introduce an authorship attribution method called Authorial
Language Models (ALMs) that involves identifying the most likely author of a
questioned document based on the perplexity of the questioned document
calculated for a set of causal language models fine-tuned on the writings of a
set of candidate author. We benchmarked ALMs against state-of-art-systems using
the CCAT50 dataset and the Blogs50 datasets. We find that ALMs achieves a
macro-average accuracy score of 83.6% on Blogs50, outperforming all other
methods, and 74.9% on CCAT50, matching the performance of the best method. To
assess the performance of ALMs on shorter texts, we also conducted text
ablation testing. We found that to reach a macro-average accuracy of 70%, ALMs
needs 40 tokens on Blogs50 and 400 tokens on CCAT50, while to reach 60% ALMs
requires 20 tokens on Blogs50 and 70 tokens on CCAT50.
