Hybrid Batch Training for Enhanced Multilingual Reasoning in Downstream Tasks

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 the proposed hybrid batch training strategy improve multilingual reasoning capabilities in downstream tasks such as XNLI or TyDi QA, compared to models trained solely for monolingual or cross-lingual retrieval?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.1/10.