Hybrid Batch Training and Multilingual Adapters for Efficient Zero-Shot Cross-Lingual 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: How does the combination of hybrid batch training and multilingual adapter layers affect the performance trade-offs between zero-shot cross-lingual retrieval accuracy and computational efficiency on the CREST benchmark compared to standard fine-tuning methods?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.6/10.