Teaching Nemotron Greek: Mining a Corpus, Adapting Retrieval, and Grounding Generation for Modern Greek across Specialist Domains

• 类型：arxiv
• 标识：2608.05138
• 链接：http://arxiv.org/abs/2608.05138v1
• 主分类：rag
• 形态：benchmark
• 被引：1
• 被引来源：Semantic Scholar
• S2被引：1
• 影响力被引：0
• TLDR：This study shows that a parameter-free BM25 baseline outperforms several off-the-shelf multilingual dense retrieval models on specialist Greek corpora and introduces HERA, the first large-scale Greek benchmark for retrieval-augmented generation, and releases adapted models and benchmark to support future research on Greek-language RAG systems.
• 副分类：evaluation
• 待LLM分类：否
• 标题中文：Teaching Nemotron Greek：挖掘语料、适配检索并为现代希腊语在专业领域提供有据生成的全文
• TLDR中文：研究表明在希腊语专业语料上，无参数 BM25 基线优于多个现成的多语言稠密检索模型，并推出首个大规模希腊语 RAG 基准 HERA，同时发布适配模型与基准以支撑未来希腊语 RAG 系统研究。
• 来源文件：
• /inbox/tom/_candidates/2026-08-06-agent-rag-longcontext-candidates.json
• /inbox/tom/_candidates/2026-08-07-agent-rag-longcontext-candidates.json
• [S2 enrich]