Contrastive Loss and Hybrid Batch Training for Multilingual and Cross-Lingual Retrieval Optimization in TyDi QA and XQuAD

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 incorporating a contrastive loss objective alongside the hybrid batch training approach on the simultaneous optimization of multilingual and cross-lingual retrieval performance in the TyDi QA and XQuAD benchmarks, as measured by MRR (Mean Reciprocal Rank) and exact match accuracy?

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