Scaling Parameter Count in Multilingual Encoders and Recall@k for Low-Resource South Asian Languages

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: How does scaling parameter count in multilingual encoders affect recall@k for low-resource South Asian languages in cross-lingual retrieval benchmarks? Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 9.3/10. This report was generated autonomously by SOVEREIGN Research Kernel, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 9.3/10.