# Exploring NLP Benchmarks in an Extremely Low-Resource Setting

arXiv: https://arxiv.org/abs/2509.03962 · HTML: https://mlsanity.com/paper/2509.03962/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 41 references that are in the map.
- First posted on arXiv 4 Sep 2025.

## Builds on (most cited first, 15 of 15)

- [Adapting Pre-trained Language Models to African Languages via Multilingual Adaptive Fine-Tuning](https://mlsanity.com/paper/2204.06487/) (2022)
- [Neural Machine Translation for Low-Resource Languages: A Survey](https://mlsanity.com/paper/2106.15115/) (2021)
- [LLMs Are Few-Shot In-Context Low-Resource Language Learners](https://mlsanity.com/paper/2403.16512/) (2024)
- [Factuality of Large Language Models: A Survey](https://mlsanity.com/paper/2402.02420/) (2024)
- [Democratizing LLMs for Low-Resource Languages by Leveraging their English Dominant Abilities with Linguistically-Diverse Prompts](https://mlsanity.com/paper/2306.11372/) (2023)
- [Language Models Do Hard Arithmetic Tasks Easily and Hardly Do Easy Arithmetic Tasks](https://mlsanity.com/paper/2406.02356/) (2024)
- [Enhancing Large Language Model Performance To Answer Questions and Extract Information More Accurately](https://mlsanity.com/paper/2402.01722/) (2024)
- [Transcending Language Boundaries: Harnessing LLMs for Low-Resource Language Translation](https://mlsanity.com/paper/2411.11295/) (2024)
- [Addressing word-order Divergence in Multilingual Neural Machine Translation for extremely Low Resource Languages](https://mlsanity.com/paper/1811.00383/) (2018)
- [LexC-Gen: Generating Data for Extremely Low-Resource Languages with Large Language Models and Bilingual Lexicons](https://mlsanity.com/paper/2402.14086/) (2024)
- [Beyond Data Quantity: Key Factors Driving Performance in Multilingual Language Models](https://mlsanity.com/paper/2412.12500/) (2024)
- [Analysis of Multi-Source Language Training in Cross-Lingual Transfer](https://mlsanity.com/paper/2402.13562/) (2024)
- [Rule-Based, Neural and LLM Back-Translation: Comparative Insights from a Variant of Ladin](https://mlsanity.com/paper/2407.08819/) (2024)
- [LLMs for Extremely Low-Resource Finno-Ugric Languages](https://mlsanity.com/paper/2410.18902/) (2024)
- [Empowering Persian LLMs for Instruction Following: A Novel Dataset and Training Approach](https://mlsanity.com/paper/2407.11186/) (2024)

## Abstract (arXiv)

The effectiveness of Large Language Models (LLMs) diminishes for extremely low-resource languages, such as indigenous languages, primarily due to the lack of labeled data. Despite growing interest, the availability of high-quality natural language processing (NLP) datasets for these languages remains limited, making it difficult to develop robust language technologies. This paper addresses such gap by focusing on Ladin, an endangered Romance language, specifically targeting the Val Badia variant. Leveraging a small set of parallel Ladin-Italian sentence pairs, we create synthetic datasets for sentiment analysis and multiple-choice question answering (MCQA) by translating monolingual Italian data. To ensure linguistic quality and reliability, we apply rigorous filtering and back-translation procedures in our method. We further demonstrate that incorporating these synthetic datasets into machine translation training leads to substantial improvements over existing Italian-Ladin translation baselines. Our contributions include the first publicly available sentiment analysis and MCQA datasets for Ladin, establishing foundational resources that can support broader NLP research and downstream applications for this underrepresented language.