LAUSR

This study introduces a Romanian–English bilingual corpus and a fine-tuned cross-lingual embedding framework aimed at improving retrieval performance in Retrieval-Augmented Generation (RAG) systems. The dataset integrates over 130,000 unstructured question–answer pairs derived from SQuAD and 9750 Romanian-generated questions linked to governmental tabular data, subsequently translated bidirectionally to build parallel Romanian–English resources. Multiple state-of-the-art embedding models, including multilingual-e5, Jina-v3, and the Qwen3-Embeddings family, were systematically evaluated on both text and tabular inputs across four language directions (eng-eng, ro-ro, eng-ro, ro-eng). The results show that while multilingual-e5-large achieved the strongest monolingual retrieval performance, Qwen3-Embedding-4B provided the best overall balance across languages and modalities. Fine-tuning this model using Low-Rank Adaptation (LoRA) and InfoNCE loss improved its Mean Reciprocal Rank (MRR) from 0.4496 to 0.4872 (+8.36%), with the largest gains observed in cross-lingual retrieval tasks. The research highlights persistent challenges in structured (tabular) data retrieval due to dataset imbalance and outlines future directions including dataset expansion, translation refinement, and instruction-based fine-tuning. Overall, this work contributes new bilingual analyses and methodological insights for advancing embedding-based retrieval in low-resource and multimodal contexts.