Impact of Hybrid Batch Training on Zero-Shot Multilingual QA Accuracy Versus Single-Objective 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: How does hybrid batch training for simultaneous monolingual and cross-lingual optimization affect zero-shot reasoning accuracy on multilingual QA benchmarks like XQuAD and MLQA compared to single-objective fine-tuning?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.2/10.