Comparative Analysis of Synergistic Batch Training versus Fine-Tuning for Zero-Shot Cross-Lingual Retrieval

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: How does the synergistic batch training strategy compare to fine-tuning on individual tasks (monolingual vs. cross-lingual) when evaluated on zero-shot retrieval benchmarks like XLM-R or MTOP?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.9/10.