Synergistic Optimization for Zero-Shot Cross-Lingual Retrieval in Low-Resource Languages on TyDi QA

Information retrieval across different languages is an increasingly important challenge in natural language processing. Recent approaches based on multilingual pre-trained language models have achieved remarkable success, yet they often optimize for either monolingual, cross-lingual, or multilingual retrieval performance at the expense of others. This paper proposes a novel hybrid batch training strategy to simultaneously improve zero-shot retrieval performance across monolingual, cross-lingual, and multilingual settings while mitigating language bias. The approach fine-tunes multilingual lang Research goal: How does the proposed synergistic optimization strategy compare to existing multilingual pre-trained language models (e.g., mBERT, XLM-R) in terms of zero-shot cross-lingual retrieval accuracy on the TyDi QA benchmark, particularly for languages with limited training data? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.9/10. This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 7.9/10.