Hybrid Batch Training vs Gradient Accumulation in M2M-100 for Low-Resource BEIR Performance

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 hybrid batch training strategy compare to gradient accumulation techniques in terms of convergence speed and zero-shot cross-lingual retrieval performance when applied to M2M-100 on the BEIR benchmark for low-resource languages?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.5/10.