Batch Size and Data Ratio Variations in Hybrid Training for Multilingual Retrieval Performance

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 varying batch size and monolingual-to-cross-lingual data ratios in hybrid batch training on the retrieval accuracy of multilingual models evaluated on BEIR and XTREME-R benchmarks?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.0/10.