Performance Comparison of Hybrid Batch Training and Domain-Adaptive Fine-Tuning on XTREME-R for Zero-Shot Cross-Lingual Retrieval

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's performance compare to domain-adaptive fine-tuning when evaluated on the XTREME-R benchmark for zero-shot cross-lingual retrieval across typologically diverse languages?

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