ORPHEAS: A Cross-Lingual Greek-English Embedding Model for Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2604.20666v1
• Date: Wed, 22 Apr 2026 15:15:45 GMT
• Title: ORPHEAS: A Cross-Lingual Greek-English Embedding Model for Retrieval-Augmented Generation
• Abstract summary: We propose ORPHEAS, a specialized Greek--English embedding model for bilingual retrieval-augmented generation.<n> ORPHEAS is trained with a high quality dataset generated by a knowledge graph-based fine-tuning methodology.<n>Experiments across monolingual and cross-lingual retrieval benchmarks reveal that ORPHEAS outperforms state-of-the-art multilingual embedding models.
• Score: 39.1609547386199
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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