Sui Generis: Large Language Models for Authorship Attribution and Verification in Latin
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912069897420800 |
|---|---|
| author | Schmidt, Gleb Gorovaia, Svetlana Yamshchikov, Ivan P. |
| author_facet | Schmidt, Gleb Gorovaia, Svetlana Yamshchikov, Ivan P. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_09245 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Sui Generis: Large Language Models for Authorship Attribution and Verification in Latin Schmidt, Gleb Gorovaia, Svetlana Yamshchikov, Ivan P. Computation and Language 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. |
| title | Sui Generis: Large Language Models for Authorship Attribution and Verification in Latin |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2410.09245 |