Impact of SWIM-IR Synthetic Data Scaling on Cross-Lingual Zero-Shot Retrieval Accuracy in XTREME

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: Does increasing synthetic training data volume from 100k to 1 million samples using SWIM-IR improve cross-lingual zero-shot retrieval accuracy on the XTREME benchmark compared to monolingual performance gains?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.5/10.