Hybrid Batch Size Variations and Monolingual-Cross-Lingual Retrieval Alignment in Multilingual Fine-Tuning

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 hybrid batch sizes on the alignment between monolingual and cross-lingual retrieval performances in models fine-tuned on multilingual datasets like CC100 or mC4?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.5/10.