@inproceedings{
li2023from,
title={From Classification to Generation: Insights into Crosslingual Retrieval Augmented {ICL}},
author={Xiaoqian Li and Ercong Nie and Sheng Liang},
booktitle={NeurIPS 2023 Workshop on Instruction Tuning and Instruction Following},
year={2023},
url={https://openreview.net/forum?id=KLPLCXo4aD}
}
The remarkable ability of Large Language Models (LLMs) to understand and follow instructions has sometimes been limited by their in-context learning (ICL) performance in low-resource languages. To address this, we introduce a novel approach that leverages cross-lingual retrieval-augmented in-context learning (CREA-ICL). By extracting semantically similar prompts from high-resource languages, we aim to bolster the zero-shot performance of multilingual pretrained language models (MPLMs) across diverse tasks. Though our approach yields steady improvements in classification tasks, it faces challenges in generation tasks, with Bangla serving as a key case study. Our evaluation offers insights into the performance dynamics of retrieval-augmented in-context learning across both classification and generation domains.
From Classification to Generation: Insights into Crosslingual Retrieval Augmented ICL
Xiaoqian Li Ercong Nie Sheng Liang