Scaling Hybrid Batch Size in Zero-Shot Cross-Lingual Retrieval and F1 Score Stability in XQuAD

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 scaling of hybrid batch size affect the inference throughput (queries per second) of zero-shot cross-lingual retrieval in language models, and how does this trade-off compare to the F1 score stability observed in XQuAD monolingual settings?

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