Multilingual Reasoning Tasks in Hybrid Batch Training for Retrieval Model Alignment

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 effect of incorporating multilingual reasoning tasks (e.g., from MMLU or BIG-Bench) into hybrid batch training on the alignment between monolingual accuracy and cross-lingual generalization in multilingual retrieval models?

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