Simultaneous Optimization of Monolingual and Cross-Lingual Objectives for Low-Resource Multilingual QA Reasoning

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: To what extent does simultaneous optimization of monolingual and cross-lingual objectives impact the reasoning capabilities of low-resource language models on multilingual QA datasets?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.6/10.