Crosslingual Retrieval Augmented In-context Learning for Bangla
Xiaoqian Li, Ercong Nie, sheng ming liang · 2023
The promise of Large Language Models (LLMs) in Natural Language Processing has often been overshadowed by their limited performance in low-resource languages such as Bangla.To address this, our paper presents a pioneering approach that utilizes crosslingual retrieval augmented in-context learning.By strategically sourcing semantically similar prompts from high-resource language, we enable multilingual pretrained language models (MPLMs), especially the generative model BLOOMZ, to successfully boost performance on Bangla tasks.Our extensive evaluation highlights that the cross-lingual retrieval augmented prompts bring steady improvements to MPLMs over the zero-shot performance.