Scaling Pre-trained Language Models for Hybrid Batch Training in 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: To what extent does scaling the size of pre-trained language models influence the effectiveness of the hybrid batch training strategy in improving cross-lingual retrieval performance on MS MARCO and ML-Doc benchmarks?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.0/10.