Hybrid Batch Training with Domain-Adaptive Pretraining for Zero-Shot Retrieval Performance in XGAP Benchmark

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: How does the hybrid batch training strategy with domain-adaptive pretraining affect the zero-shot retrieval performance gap between high-resource and low-resource languages on the XGAP benchmark when compared to standard multilingual pretraining approaches?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.9/10.