NVIDIA的Nemotron终于能处理希腊语了，微调后检索效果大幅提升，还新出了个HERA基准，搞多语言RAG的可以看看。
该研究将NVIDIA的Nemotron检索栈端到端适配为现代希腊语，覆盖法律、能源、金融和医疗等专业领域。在65,773个希腊语检索对上微调后，Nemotron 1B嵌入模型的nDCG@10从0.362提升至0.835，显著超过未适配版本。研究还通过LoRA微调Nemotron 30B-A3B混合专家阅读器，使答案正确率从29.4%升至66.9%，并改善了忠实度和引用质量。作者发布了希腊语RAG基准HERA，这是首个大规模希腊语检索增强生成基准。
Teaching Nemotron Greek: Mining a Corpus, Adapting Retrieval, and Grounding Generation for Modern Greek across Specialist Domains
Modern Greek is absent from NVIDIA's Nemotron retrieval models and from major multilingual retrieval benchmarks, despite being important for retrieval-augmented generation (RAG) in legal, energy, financial, and medical applications. We present an end-to-end adaptation of the Nemotron retrieval stack for Modern Greek, including corpus mining, synthetic supervision, retrieval model training, reranker adaptation, reader fine-tuning, and a new benchmark called HERA. Our study shows that a parameter-free BM25 baseline outperforms 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 improves nDCG@10 from 0.362 to 0.835 and substantially outperforms its unadapted counterpart. The learned language competence transfers to general-domain Greek, although the advantage over BM25 remains domain-dependent. We further adapt a cross-encoder reranker and demonstrate consistent improvements across specialist domains. Finally, we LoRA-tune a Nemotron 30B-A3B mixture-of-experts reader for grounded generation, increasing judged answer correctness from 29.4% to 66.9% while significantly improving faithfulness and citation quality. We also introduce HERA, the first large-scale Greek benchmark for retrieval-augmented generation, and release our adapted models and benchmark to support future research on Greek-language RAG systems.