Researchers adapt Nemotron retrieval stack for Modern Greek
The work adds Greek retrieval training, reranking, grounded generation and a HERA benchmark.
Why it matters
The results show that multilingual retrieval systems can still underperform on underrepresented languages and specialist domains without targeted adaptation. They also suggest that low-resource language RAG needs benchmarks, retrievers, rerankers and readers tuned together rather than relying only on general multilingual models.
The key points
- 1.Nemotron was adapted for Modern Greek specialist-domain RAG.
- 2.Fine-tuned Nemotron 1B embedder raised nDCG@10 to 0.835.
- 3.LoRA-tuned Nemotron 30B-A3B reader improved judged correctness to 66.9%.
Researchers presented an end-to-end adaptation of NVIDIA's Nemotron retrieval stack for Modern Greek, covering corpus mining, synthetic supervision, retrieval model training, reranker adaptation, reader fine-tuning and a new HERA benchmark. The paper reports that BM25 outperformed several off-the-shelf multilingual dense retrieval models on specialist Greek corpora. After fine-tuning on 65,773 Greek retrieval pairs, a Nemotron 1B embedder improved nDCG@10 from 0.362 to 0.835, while a LoRA-tuned Nemotron 30B-A3B reader raised judged answer correctness from 29.4% to 66.9%.
⚡ Try this today
Benchmark BM25 before deploying dense retrieval for specialist Greek RAG, then fine-tune retrieval and reranking if quality lags.
Sources & original reporting
This brief summarizes and links to reporting from the publishers below.
- arXiv cs.CLTeaching Nemotron Greek: Mining a Corpus, Adapting Retrieval, and Grounding Generation for Modern Greek across Specialist DomainsAug 6, 12:00 PM↗
- arXiv cs.AITeaching Nemotron Greek: Mining a Corpus, Adapting Retrieval, and Grounding Generation for Modern Greek across Specialist DomainsAug 6, 12:00 PM↗
- HF Daily PapersTeaching Nemotron Greek: Mining a Corpus, Adapting Retrieval, and Grounding Generation for Modern Greek across Specialist DomainsAug 5, 4:00 AM↗
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