# Open or Closed LLM for Lesser-Resourced Languages? Lessons from Greek

arXiv: https://arxiv.org/abs/2501.12826 · HTML: https://mlsanity.com/paper/2501.12826/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 38 references that are in the map.
- First posted on arXiv 22 Jan 2025.

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

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [One Embedder, Any Task: Instruction-Finetuned Text Embeddings](https://mlsanity.com/paper/2212.09741/) (2022)
- [Predicting the Type and Target of Offensive Posts in Social Media](https://mlsanity.com/paper/1902.09666/) (2019)
- [Leak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in Closed-Source LLMs](https://mlsanity.com/paper/2402.03927/) (2024)
- [SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)](https://mlsanity.com/paper/2006.07235/) (2020)
- [Offensive Language Identification in Greek](https://mlsanity.com/paper/2003.07459/) (2020)
- [DIALECTBENCH: A NLP Benchmark for Dialects, Varieties, and Closely-Related Languages](https://mlsanity.com/paper/2403.11009/) (2024)
- [Meltemi: The first open Large Language Model for Greek](https://mlsanity.com/paper/2407.20743/) (2024)
- [Multi-granular Legal Topic Classification on Greek Legislation](https://mlsanity.com/paper/2109.15298/) (2021)
- [NLP for The Greek Language: A Longer Survey](https://mlsanity.com/paper/2408.10962/) (2024)

## Built on by (most cited first, 1 of 1)

- [Evaluating Monolingual and Multilingual Large Language Models for Greek Question Answering: The DemosQA Benchmark](https://mlsanity.com/paper/2602.16811/) (2026)

## Abstract (arXiv)

Natural Language Processing (NLP) for lesser-resourced languages faces persistent challenges, including limited datasets, inherited biases from high-resource languages, and the need for domain-specific solutions. This study addresses these gaps for Modern Greek through three key contributions. First, we evaluate the performance of open-source (Llama-70b) and closed-source (GPT-4o mini) large language models (LLMs) on seven core NLP tasks with dataset availability, revealing task-specific strengths, weaknesses, and parity in their performance. Second, we expand the scope of Greek NLP by reframing Authorship Attribution as a tool to assess potential data usage by LLMs in pre-training, with high 0-shot accuracy suggesting ethical implications for data provenance. Third, we showcase a legal NLP case study, where a Summarize, Translate, and Embed (STE) methodology outperforms the traditional TF-IDF approach for clustering long legal texts. Together, these contributions provide a roadmap to advance NLP in lesser-resourced languages, bridging gaps in model evaluation, task innovation, and real-world impact.