Multilingual sentence-embedding models are widely used for cross-lingual retrieval; however, their performance drops significantly in low-resource languages. The Urdu language, which is considered a low-resource language by the NL community, poses this challenge, despite being spoken by over 246 million people worldwide. Its distribution in training corpora results in poor alignment with English within shared embedding spaces. To resolve this misalignment without model fine-tuning, we apply Procrustes transformation, which is an orthogonal post-hoc alignment method with a closed-form solution. We utilize SQuAD and UQA datasets to learn a rotation matrix from a small set of sentence pairs and evaluate its effect across five multilingual embedding models (MiniLM, DistilUSE, E5-Base, LaBSE, and E5-Large) and perform geometric alignment, cross-lingual retrieval, and question-answering tasks on these models. We find that cosine distances between parallel pairs decrease by up to 38.67%, and retrieval accuracy improves by 12.49% points in Recall@1. We also analyze that models with better pre-trained cross-lingual representations exhibit a saturation effect, showing minimal retrieval change even as geometric tightening increases. Our error analysis reveals that morphologically complex queries and colloquial expressions remain challenging, indicating representational limitations beyond the scope of a linear transformation. These findings demonstrate that a computationally inexpensive alignment step can meaningfully improve cross-lingual retrieval for low-resource languages, with implications for retrieval-augmented generation (RAG) in resource-constrained settings.

Retrieval-Augmented Generation performs well on English QA benchmarks, but degrades considerably in morphologically rich, low-resource languages. Urdu presents a particularly challenging case: heavy inflectional morphology, Nastaliq script inconsistencies, and limited training data produce a systematic mismatch between query representations and indexed document content that standard retrieval architectures cannot bridge. We propose DR-RAG (Dual-Representation Retrieval-Augmented Generation), which addresses this through dual indexing. Each document is represented as overlapping text chunks and as automatically generated question-answer pairs. Queries are first matched against the QA index, which aligns more reliably with natural query phrasing than declarative document chunks. When retrieval confidence falls below τ = 0.80, the system falls back to chunk-based retrieval, maintaining coverage without sacrificing precision. Evaluated on Urdu UQA and English SQuAD 2.0, DR-RAG improves Urdu METEOR by 38×, ROUGE-1 by 140%, and reduces generation latency by 43%. LLM-as judge scores show higher faithfulness (3.03 vs 1.93) and overall quality (2.99 vs 2.21) over MultiVector. English performance remains competitive throughout. These results indicate that representation-level alignment between queries and indexed content, rather than increased model complexity, is the critical factor for reliable retrieval in underserved South Asian languages.

Medical AI systems increasingly rely on large language models (LLMs), yet their deployment in linguistically diverse regions remains unexplored. We address this gap by introducing U-MIRAGE, the first medical question-answering benchmark for Urdu and Roman Urdu. Urdu is the 11th most spoken language (with over 246 million speakers) worldwide. Our systematic evaluation of six state-of-the-art LLMs reveals three main findings. (1) 6% to 10% drop in performance when moving from English to Urdu variants, even though medical knowledge should theoretically transfer across languages. (2) Chain-of-Thought (CoT) prompting improves small models by 8% to 20%, while surprisingly the larger models’ performance degraded by up to 3%. (3) Quantized small models fail catastrophically in low-resource languages, achieving near-random accuracy regardless of various prompting strategies. These findings challenge core assumptions about multilingual medical AI systems. Roman Urdu consistently outperforms standard Urdu script, suggesting orthographic alignment with pre-training data matters more than linguistic proximity. CoT prompting effectiveness depends critically on model architecture rather than task complexity alone. CoT prompting effectiveness depends critically on model architecture rather than task complexity alone. Our contributions are threefold: (1) U-MIRAGE, (2) systematic benchmarking of LLMs for Urdu and Roman Urdu medical reasoning, and (3) empirical analysis of CoT prompting in low-resource contexts. Our code and datasets are publicly available.