Hybrid Batch Training Strategies and Retrieval Performance in MIRACL Benchmark

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: What is the impact of varying the ratio of monolingual, cross-lingual, and multilingual samples in the hybrid batch training strategy on retrieval performance (MAP and NDCG) across different language pairs in the MIRACL benchmark?

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