Simultaneous Optimization Effects on Multilingual Language Model Reasoning in Cross-Lingual 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: Does simultaneous optimization of monolingual and cross-lingual objectives degrade reasoning capabilities in multilingual language models when evaluated on cross-lingual QA benchmarks?

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