MarathiXRAG: Cross-Lingual Retrieval for Low-Resource Indic Languages
Abstract
Abstract This technical report presents MarathiXRetrieve, an empirical study on mitigating the cross-lingual performance penalty in dense retrieval systems for low-resource languages. We develop and evaluate a complete end-to-end pipeline that synthetically generates aligned evaluation datasets and applies contrastive fine-tuning to the BAAI/bge-m3 multilingual embedding model, targeting Marathi-to-English document retrieval scenarios. Our core hypothesis — that targeted contrastive fine-tuning on synthetically aligned, article-aware language pairs can structurally reorganize the joint vector space to mitigate cross-lingual penalties without catastrophic forgetting — is validated through comprehensive experimentation. The fine-tuned model achieves a +2.84% absolute improvement in cross-lingual Top-1 retrieval accuracy and a +3.91% improvement in NDCG@10. Crucially, the performance gap between English and Marathi Top-1 retrieval is compressed from 7.93% to 4.17%, effectively reducing the cross-lingual penalty by 47.4%, while monolingual English performance degrades by only -0.92%, confirming preservation of baseline knowledge. The report covers the full system architecture, including (1) synthetic Q&A generation using Qwen2.5-7B-Instruct-AWQ on vLLM, (2) neural translation via sarvam-translate, (3) vector indexing with ChromaDB and BGE-M3, (4) contrastive optimization using MultipleNegativesRankingLoss, and (5) evaluation via pytrec_eval with a novel 3-tier graded relevance…
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