Zero-shot cross-lingual retrieval performance scaling with language diversity in SWIM-IR 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 models trained on SWIM-IR synthetic data scale with the number of languages included in the training set, evaluated on the XQuAD benchmark across varying language resource levels?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.9/10.