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 domain adaptation techniques on the zero-shot cross-lingual retrieval capabilities of models trained with the proposed hybrid batch strategy, as measured by BEIR benchmarks across diverse language pairs? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.0/10.

This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 8.0/10.