Scalability of Hybrid Batch Training for Multilingual Pre-training on BEIR Cross-lingual Retrieval

Abstract

Abstract: 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 Question

How does the proposed hybrid batch training strategy scale in terms of computational efficiency (measured in training time and memory usage) when increasing the number of languages in the training set, compared to traditional multilingual pre-training approaches like mBERT, as evaluated on the BEIR cross-lingual retrieval benchmark?

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