Scaling Zero-Shot Cross-Lingual Retrieval Performance with Target Languages in Hybrid Training

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 zero-shot cross-lingual retrieval performance of the proposed hybrid training approach scale with increasing numbers of target languages, as measured by MRR on MIRACL and NDQA benchmarks?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.3/10.