Synergistic Optimization of Monolingual, Cross-lingual, and Multilingual Objectives for XLQA Retrieval in Unseen Low-resource

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: Does the synergistic optimization of monolingual, cross-lingual, and multilingual objectives improve retrieval performance on unseen low-resource language pairs in the XLQA benchmark compared to isolated training approaches? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.3/10. This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 8.3/10.