Less is More: Adapting Text Embeddings for Low-Resource Languages with Small Scale Noisy Synthetic Data
Low-resource languages (LRLs) often lack high-quality, large-scale datasets for training effective text embedding models, hindering their application in tasks like retrieval-augmented generation (RAG) and semantic search. In this work, we challenge the prevailing assumption that effective semantic alignment requires massive datasets or pristine, human-verified translations. Focusing on Armenian (an LRL with a unique script), we introduce a cost-effective adaptation strategy using small scale noisy synthetic data generated by translating English Reddit title-body pairs with open-weights models. We establish a comprehensive evaluation benchmark comprising existing datasets, translated data,...
| Authors | Zaruhi Navasardyan, Spartak Bughdaryan, Bagrat Minasyan et al. |
|---|---|
| Primary category | cs.CL |
| Published | 4 March 2026 |
| Last updated at source | 4 March 2026 |
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- Metric-AI/armenian-text-embeddings-2-largeModel · cited by
- Metric-AI/armenian-text-embeddings-2-baseModel · cited by
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