# Evaluating Large Language Models for Diacritic Restoration in Romanian Texts: A Comparative Study

arXiv: https://arxiv.org/abs/2511.13182 · HTML: https://mlsanity.com/paper/2511.13182/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 17 Nov 2025.

## Builds on (most cited first, 5 of 5)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Mixtral of Experts](https://mlsanity.com/paper/2401.04088/) (2024)
- [A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models](https://mlsanity.com/paper/2303.10420/) (2023)
- [OpenLLM-Ro -- Technical Report on Open-source Romanian LLMs](https://mlsanity.com/paper/2405.07703/) (2024)

## Abstract (arXiv)

Automatic diacritic restoration is crucial for text processing in languages with rich diacritical marks, such as Romanian. This study evaluates the performance of several large language models (LLMs) in restoring diacritics in Romanian texts. Using a comprehensive corpus, we tested models including OpenAI's GPT-3.5, GPT-4, GPT-4o, Google's Gemini 1.0 Pro, Meta's Llama 2 and Llama 3, MistralAI's Mixtral 8x7B Instruct, airoboros 70B, and OpenLLM-Ro's RoLlama 2 7B, under multiple prompt templates ranging from zero-shot to complex multi-shot instructions. Results show that models such as GPT-4o achieve high diacritic restoration accuracy, consistently surpassing a neutral echo baseline, while others, including Meta's Llama family, exhibit wider variability. These findings highlight the impact of model architecture, training data, and prompt design on diacritic restoration performance and outline promising directions for improving NLP tools for diacritic-rich languages.