Sui Generis：面向拉丁语文本作者归属与验证的大语言模型研究
Sui Generis: Large Language Models for Authorship Attribution and Verification in Latin
AI总结：
该研究评估大语言模型在教父时代拉丁语文本作者归属与验证任务的表现，发现其零样本验证有鲁棒性但易受语义干扰，且难引导决策，特定场景超传统基线但获可解释结果需大量实验。
摘要：
本文评估了大语言模型（LLMs）在教父时代拉丁语文本的作者归属与作者验证任务中的性能。研究表明，即便在短文本、无复杂特征工程的条件下，LLMs在零样本作者验证任务中仍具备鲁棒性，但模型也易被语义信息“误导”。实验还显示，与高资源现代语言相关研究的结论不同，引导模型开展作者分析与决策颇具难度。尽管LLMs在特定场景下能超越传统基线，但要得到细致且真正可解释的决策，充其量仍需大量实验探索。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
