Impact of Curriculum Learning on Zero-Shot Cross-Lingual Retrieval for Low-Resource Niger-Congo Languages

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 curriculum learning on the hybrid batch training strategy's zero-shot cross-lingual retrieval performance for low-resource Niger-Congo languages, compared to random sampling, when evaluated on MIRACL and XNLI benchmarks using accuracy and F1-score metrics?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.1/10.