Hybrid Batch Ratio Optimization and Retrieval Degradation in BEIR Complex Reasoning Tasks

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: Does optimizing the hybrid batch ratio for simultaneous monolingual and cross-lingual performance degrade retrieval metrics on complex reasoning tasks within the BEIR benchmark?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.2/10.