Crosslingual Retrieval Augmented In-Context Learning for Bangla

MCML Authors

Ercong Nie

Dr.

* Former Member

→ Group Hinrich Schütze
 Computational Linguistics

Sheng Liang

* Former Member

→ Group Hinrich Schütze
 Computational Linguistics

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.

inproceedings LNL23a

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BLP 2023

1st Workshop on Bangla Language Processing. Singapore, Dec 07, 2023.

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Authors

X. Li • E. Nie • S. Liang

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Links

DOI

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Research Area

 B2 | Natural Language Processing