Domain-Adaptive Fine-Tuning and Hybrid Batch Training in Cross-Lingual XQuAD Retrieval

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 effect of integrating domain-adaptive fine-tuning with the proposed hybrid batch training on cross-lingual retrieval performance in the XQuAD benchmark, measured by changes in EM and F1 scores across different language pairs?

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