专为希腊语和英语设计的检索增强生成嵌入模型，提升跨语言语义对齐效果。
ORPHEAS: A Cross-Lingual Greek-English Embedding Model for Retrieval-Augmented Generation
- 基于知识图谱构建高质量数据集，针对希腊语复杂形态进行微调。
- 在多领域语料上训练，实现跨语言检索性能超越现有主流模型。
- 适合需要高精度双语信息检索的研究者与开发者使用。
在双语希腊语-英语应用中实现高效的检索增强生成，需要能够捕捉领域特定语义关系和跨语言语义对齐的嵌入模型。现有的多语言嵌入模型将表征能力分散于众多语言，限制了其对希腊语的优化，并难以编码希腊语文本中固有的形态复杂性与领域术语结构。本文提出 ORPHEAS，一种专用于希腊语-英语的检索增强生成嵌入模型。ORPHEAS 采用基于知识图谱的微调方法，构建高质量数据集，应用于多样化多领域语料，实现语言无关的语义表示。数值实验在单语和跨语言检索基准上均表明，ORPHEAS 性能优于当前最先进的多语言嵌入模型，证明对形态复杂的语言进行领域专用微调，不会损害跨语言检索能力。
原文摘要 · Abstract (English)
Effective retrieval-augmented generation across bilingual Greek--English applications requires embedding models capable of capturing both domain-specific semantic relationships and cross-lingual semantic alignment. Existing multilingual embedding models distribute their representational capacity across numerous languages, limiting their optimization for Greek and failing to encode the morphological complexity and domain-specific terminological structures inherent in Greek text. In this work, we propose ORPHEAS, a specialized Greek--English embedding model for bilingual retrieval-augmented generation. ORPHEAS is trained with a high quality dataset generated by a knowledge graph-based fine-tuning methodology which is applied to a diverse multi-domain corpus, which enables language-agnostic semantic representations. The numerical experiments across monolingual and cross-lingual retrieval benchmarks reveal that ORPHEAS outperforms state-of-the-art multilingual embedding models, demonstrating that domain-specialized fine-tuning on morphologically complex languages does not compromise cross-lingual retrieval capability.
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