# Evaluating OpenAI GPT Models for Translation of Endangered Uralic Languages: A Comparison of Reasoning and Non-Reasoning Architectures

arXiv: https://arxiv.org/abs/2512.16287 · HTML: https://mlsanity.com/paper/2512.16287/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 11 of its 28 references that are in the map.
- First posted on arXiv 18 Dec 2025.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 11 of 11)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation](https://mlsanity.com/paper/1611.04558/) (2016)
- [The first neural machine translation system for the Erzya language](https://mlsanity.com/paper/2209.09368/) (2022)
- [Neural Morphology Dataset and Models for Multiple Languages, from the Large to the Endangered](https://mlsanity.com/paper/2105.12428/) (2021)
- [The Current State of Finnish NLP](https://mlsanity.com/paper/2109.11326/) (2021)
- [A Comparative Analysis of Ethical and Safety Gaps in LLMs using Relative Danger Coefficient](https://mlsanity.com/paper/2505.04654/) (2025)
- [Sentiment Analysis Using Aligned Word Embeddings for Uralic Languages](https://mlsanity.com/paper/2305.15380/) (2023)
- [Efficient Toxicity Detection in Gaming Chats: A Comparative Study of Embeddings, Fine-Tuned Transformers and LLMs](https://mlsanity.com/paper/2510.17924/) (2025)

## Built on by (most cited first, 2 of 2)

- [Tokenization and Morphological Fidelity in Uralic NLP: A Cross-Lingual Evaluation](https://mlsanity.com/paper/2602.04241/) (2026)
- [A Komi-Yazva--Russian Parallel Corpus and Evaluation Protocol for Zero- and Few-Shot LLM Translation](https://mlsanity.com/paper/2606.06420/) (2026)

## Abstract (arXiv)

The evaluation of Large Language Models (LLMs) for translation tasks has primarily focused on high-resource languages, leaving a significant gap in understanding their performance on low-resource and endangered languages. This study presents a comprehensive comparison of OpenAI's GPT models, specifically examining the differences between reasoning and non-reasoning architectures for translating between Finnish and four low-resource Uralic languages: Komi-Zyrian, Moksha, Erzya, and Udmurt. Using a parallel corpus of literary texts, we evaluate model willingness to attempt translation through refusal rate analysis across different model architectures. Our findings reveal significant performance variations between reasoning and non-reasoning models, with reasoning models showing 16 percentage points lower refusal rates. The results provide valuable insights for researchers and practitioners working with Uralic languages and contribute to the broader understanding of reasoning model capabilities for endangered language preservation.