# Detecting Latin in Historical Books with Large Language Models: A Multimodal Benchmark

arXiv: https://arxiv.org/abs/2510.19585 · HTML: https://mlsanity.com/paper/2510.19585/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 53 references that are in the map.
- First posted on arXiv 22 Oct 2025.

## Builds on (most cited first, 16 of 16)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI](https://mlsanity.com/paper/2311.16502/) (2023)
- [InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models](https://mlsanity.com/paper/2504.10479/) (2025)
- [Gemma 3 Technical Report](https://mlsanity.com/paper/2503.19786/) (2025)
- [LayoutLLM: Layout Instruction Tuning with Large Language Models for Document Understanding](https://mlsanity.com/paper/2404.05225/) (2024)
- [LinCE: A Centralized Benchmark for Linguistic Code-switching Evaluation](https://mlsanity.com/paper/2005.04322/) (2020)
- [Document Collection Visual Question Answering](https://mlsanity.com/paper/2104.14336/) (2021)
- [A Fast, Compact, Accurate Model for Language Identification of Codemixed Text](https://mlsanity.com/paper/1810.04142/) (2018)
- [OCR Error Post-Correction with LLMs in Historical Documents: No Free Lunches](https://mlsanity.com/paper/2502.01205/) (2025)
- [Sui Generis: Large Language Models for Authorship Attribution and Verification in Latin](https://mlsanity.com/paper/2410.09245/) (2024)
- [UDPipe at EvaLatin 2020: Contextualized Embeddings and Treebank Embeddings](https://mlsanity.com/paper/2006.03687/) (2020)
- [LatinCy: Synthetic Trained Pipelines for Latin NLP](https://mlsanity.com/paper/2305.04365/) (2023)
- [Lexical semantic change for Ancient Greek and Latin](https://mlsanity.com/paper/2101.09069/) (2021)
- [Reception Reader: Exploring Text Reuse in Early Modern British Publications](https://mlsanity.com/paper/2302.04084/) (2023)

## Abstract (arXiv)

This paper presents a novel task of extracting low-resourced and noisy Latin fragments from mixed-language historical documents with varied layouts. We benchmark and evaluate the performance of large foundation models against a multimodal dataset of 724 annotated pages. The results demonstrate that reliable Latin detection with contemporary zero-shot models is achievable, yet these models lack a functional comprehension of Latin. This study establishes a comprehensive baseline for processing Latin within mixed-language corpora, supporting quantitative analysis in intellectual history and historical linguistics. Both the dataset and code are available at https://github.com/COMHIS/EACL26-detect-latin.