Zero-Shot Cross-Lingual Retrieval Robustness Under Varying Low-Resource Language Proportions in Code-Switched Training Data

Benefiting from transformer-based pre-trained language models, neural ranking models have made significant progress. More recently, the advent of multilingual pre-trained language models provides great support for designing neural cross-lingual retrieval models. However, due to unbalanced pre-training data in different languages, multilingual language models have already shown a performance gap between high and low-resource languages in many downstream tasks. And cross-lingual retrieval models built on such pre-trained models can inherit language bias, leading to suboptimal result for low-reso Research goal: What is the impact of varying the proportion of low-resource languages in code-switched training data on the robustness of zero-shot cross-lingual retrieval models, as evaluated by accuracy and F1 score on out-of-domain datasets like MLQA? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.5/10. This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 8.5/10.