Simultaneous Monolingual and Cross-Lingual Optimization for Robustness Against Domain Shift in Multilingual Information Retrieval

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: What is the impact of simultaneous monolingual and cross-lingual optimization on robustness against domain shift in multilingual information retrieval benchmarks?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.5/10.