  The emergence of Large Language Models (LLMs) with capabilities like
In-Context Learning (ICL) has ushered in new possibilities for data generation
across various domains while minimizing the need for extensive data collection
and modeling techniques. Researchers have explored ways to use this generated
synthetic data to optimize smaller student models for reduced deployment costs
and lower latency in downstream tasks. However, ICL-generated data often
suffers from low quality as the task specificity is limited with few examples
used in ICL. In this paper, we propose GeMQuAD - a semi-supervised learning
approach, extending the WeakDAP framework, applied to a dataset generated
through ICL with just one example in the target language using AlexaTM 20B
Seq2Seq LLM. Through our approach, we iteratively identify high-quality data to
enhance model performance, especially for low-resource multilingual setting in
the context of Extractive Question Answering task. Our framework outperforms
the machine translation-augmented model by 0.22/1.68 F1/EM (Exact Match) points
for Hindi and 0.82/1.37 F1/EM points for Spanish on the MLQA dataset, and it
surpasses the performance of model trained on an English-only dataset by
5.05/6.50 F1/EM points for Hindi and 3.81/3.69 points F1/EM for Spanish on the
same dataset. Notably, our approach uses a pre-trained LLM for generation with
no fine-tuning (FT), utilizing just a single annotated example in ICL to
generate data, providing a cost-effective development process.
