--- title: "Who cites Optimized Text Embedding Models — 2 citing papers" description: "2 papers of the Elamur Research library cite «Optimized Text Embedding Models and Benchmarks for Amharic Passage Retrieval», found in their…" canonical: "https://research.elamur.ai/free/en/papers/mekonnen-2025-optimized-text-embedding-models-benchmarks/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/mekonnen-2025-optimized-text-embedding-models-benchmarks/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Optimized Text Embedding Models and Benchmarks…](https://md.research.elamur.ai/free/en/papers/mekonnen-2025-optimized-text-embedding-models-benchmarks/) / cited by # Who cites «Optimized Text Embedding Models and Benchmarks for Amharic Passage Retrieval» 2 papers of our library cite it (of the 149,439 whose bibliographies we read) · 2 with a page here, the most cited first · [← back to «Optimized Text Embedding Models and Benchmarks for Amharic Passage Retrieval»](https://md.research.elamur.ai/free/en/papers/mekonnen-2025-optimized-text-embedding-models-benchmarks/) 1. [**AmharicIR+Instr: A Two-Dataset Resource for Neural Retrieval and Instruction Tuning arXiv:2602.09914**Tilahun Yeshambel, Moncef Garouani, Josiane Mothe*2026*](https://md.research.elamur.ai/free/en/papers/yeshambel-2026-amharicir-instr-two-dataset-resource/) 2. **[The Multilingual Curse at the Retrieval Layer: Evidence from Amharic](https://md.research.elamur.ai/free/en/papers/alemneh-2026-multilingual-curse-retrieval-layer-evidence/) [Workshop on Multilinguality in the Era of Large Language Models](https://md.research.elamur.ai/free/en/venues/mellm/)**Yosef Worku Alemneh, Kidist Amde Mekonnen, Maarten de Rijke*2026* - [← back to](https://md.research.elamur.ai/free/en/papers/mekonnen-2025-optimized-text-embedding-models-benchmarks/) — Optimized Text Embedding Models and Benchmarks for Amharic Passage Retrieval