Hybrid Batch Scaling in Multilingual Encoders: Retrieval Performance Trade-offs

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 scaling the size of the hybrid batch training dataset on the trade-off between monolingual and cross-lingual retrieval performance in multilingual encoders, as measured by BEIR and MTEB benchmarks?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.5/10.