Impact of Mixed-Language Batch Training on Cross-Lingual Transfer Performance in Multilingual LLMs

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 mixed-language batch training on the cross-lingual transfer performance of multilingual LLMs like LLaMA on tasks such as XNLI and PAWS-X, compared to monolingual batch training?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.7/10.