Crosslingual Retrieval Augmented In-context Learning for Bangla

• URL: http://arxiv.org/abs/2311.00587v2
• Date: Sat, 2 Dec 2023 16:54:23 GMT
• Title: Crosslingual Retrieval Augmented In-context Learning for Bangla
• Authors: Xiaoqian Li, Ercong Nie, Sheng Liang
• Abstract summary: This paper presents a pioneering approach that utilizes cross-lingual retrieval augmented in-context learning. By strategically sourcing semantically similar prompts from high-resource language, we enable multilingual pretrained language models (MPLMs) to successfully boost performance on Bangla tasks. Our evaluation highlights that the cross-lingual retrieval augmented prompts bring steady improvements to MPLMs over the zero-shot performance.
• Score: 8.065775937617417
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 cross-lingual 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.

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