Analysing LLMs for spelling normalization of 18th century Portuguese

2026-04-12 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

Researchers evaluated several large language models (LLMs) for normalizing 18th-century Portuguese texts to contemporary orthography. The study compared the outputs of these LLMs against a meticulously curated reference corpus. Key findings revealed substantial differences in performance among the models tested. Specifically, the Portuguese-specialized model Sabiá achieved a statistically significant performance advantage over its multilingual counterparts, indicating the benefit of domain-specific training for historical language normalization tasks.

Key takeaway

For research scientists working on historical text processing, you should prioritize evaluating and deploying language-specific LLMs over general multilingual models. The Sabiá model's superior performance in normalizing 18th-century Portuguese suggests that specialized training offers a critical advantage for tasks requiring high orthographic accuracy, potentially reducing post-processing effort and improving data quality.

Key insights

Specialized LLMs significantly outperform multilingual models for historical language normalization.

Principles

• Domain-specific models excel in specialized linguistic tasks.

Method

LLMs processed pre-contemporary Portuguese texts, with outputs rigorously compared against a curated reference corpus for normalization accuracy.

In practice

• Prioritize specialized LLMs for historical text processing.
• Use curated corpora for robust model evaluation.

Topics

• Large Language Models
• Spelling Normalization
• 18th Century Portuguese
• Sabiá Model
• Historical Text Processing

Best for: Research Scientist, AI Scientist, NLP Engineer

Related on AIssential

• 18th-century Fauna and Flora: from Named Entities to the problems of standardization — Lexical variation in historical specialized texts presents significant challenges for computational processing.
• A Larger Annotated Corpus of Portuguese Coreference — LLMs can annotate Portuguese coreference, but a new LLM-based system did not outperform existing rule-based methods.
• Cross-Lingual Empirical Evaluation of Large Language Models for Arabic Medical Tasks — English-centric LLMs exhibit significant performance gaps and reliability issues in Arabic medical tasks.
• PorTEXTO: A European Portuguese Benchmark for Visual Text Extraction — Specialized multilingual data significantly improves European Portuguese OCR performance over model size or resolution.
• Evaluating LLM-Based Translation of a Low-Resource Technical Language: The Medical and Philosophical Greek of Galen — LLMs translate Ancient Greek technical texts with high accuracy, but rare terminology significantly predicts failure.
• Equity with Efficiency: An Empirical Study of Tokenizers for Multilingual Large Language Models — Cross-lingual fairness and tokenization efficiency are not fundamentally at odds in multilingual LLMs.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.