Sui Generis: Large Language Models for Authorship Attribution and Verification in Latin
This addresses authorship analysis for low-resource historical Latin texts, though it is incremental in applying existing LLMs to a new domain.
This paper evaluated Large Language Models for authorship attribution and verification of Latin texts from the Patristic Era, finding they can be robust in zero-shot verification on short texts without feature engineering but are easily misled by semantics and challenging to steer for nuanced decisions.
This paper evaluates the performance of Large Language Models (LLMs) in authorship attribution and authorship verification tasks for Latin texts of the Patristic Era. The study showcases that LLMs can be robust in zero-shot authorship verification even on short texts without sophisticated feature engineering. Yet, the models can also be easily "mislead" by semantics. The experiments also demonstrate that steering the model's authorship analysis and decision-making is challenging, unlike what is reported in the studies dealing with high-resource modern languages. Although LLMs prove to be able to beat, under certain circumstances, the traditional baselines, obtaining a nuanced and truly explainable decision requires at best a lot of experimentation.