mlsanityData to 7 Oct 2026

Sui Generis: Large Language Models for Authorship Attribution and Verification in Latin

arXiv 2410.09245 ↗First posted 11 Oct 2024cs.CLcited by 8

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Abstract (arXiv)

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.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 12 of its 51 references that are in the map. First posted on arXiv 11 Oct 2024.

Builds on

12 of 51 references.

References · 12

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022Training language models to follow instructions with human feedbackcited by 15,712
  3. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  4. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,222
  5. 2022From Attribution Maps to Human-Understandable Explanations through Concept Relevance Propagationcited by 141
  6. 2020Latin BERT: A Contextual Language Model for Classical Philologycited by 43
  7. 2024Can Large Language Models Identify Authorship?cited by 40
  8. 2024Best Practices for Text Annotation with Large Language Modelscited by 34
  9. 2023Can Authorship Representation Learning Capture Stylistic Features?cited by 17
  10. 2022PART: Pre-trained Authorship Representation Transformercited by 13
  11. 2023Machine-assisted quantitizing designs: augmenting humanities and social sciences with artificial intelligencecited by 13
  12. 2021Syllabic Quantity Patterns as Rhythmic Features for Latin Authorship Attributioncited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 6; 2026: 2.

8 papers.

  1. 2025Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approachescited by 2
  2. 2025Better Call Claude: Can LLMs Detect Changes of Writing Style?cited by 1
  3. 2025Contextual morphologically-guided tokenization for Latin encoder modelscited by 1
  4. 2026De-Anonymization at Scale via Tournament-Style Attributioncited by 1
  5. 2025What's in a prompt? Language models encode literary style in prompt embeddingscited by 0
  6. 2025Smotrom tvoja pa ander drogoj verden! Resurrecting Dead Pidgin with Generative Models: Russenorsk Case Studycited by 0
  7. 2025Detecting Latin in Historical Books with Large Language Models: A Multimodal Benchmarkcited by 0
  8. 2026RespondeoQA: a Benchmark for Bilingual Latin-English Question Answeringcited by 0