Hybrid Batch Training vs. Adapter-Based Fine-Tuning for Low-Resource Zero-Shot Retrieval on XLM-R

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 hybrid batch training strategy compare to adapter-based fine-tuning in improving zero-shot retrieval performance for low-resource languages on the XLM-R benchmark, measured by P@1 and MRR@5?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.1/10.