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.

翻译：大型语言模型（LLMs）的涌现带来了上下文学习（ICL）等能力，为各领域的数据生成开辟了新的可能性，同时极大减少了广泛的数据收集和建模技术需求。研究者已探索如何利用这些生成的合成数据优化较小的学生模型，以降低下游任务的部署成本和延迟。然而，ICL生成的数据常因任务特异性不足而质量较低——其使用的少量示例限制了任务适应性。本文提出GeMQuAD——一种半监督学习方法，扩展了WeakDAP框架，应用于通过ICL仅使用目标语言中的一个示例、基于AlexaTM 20B Seq2Seq LLM生成的数据集。通过该方法，我们迭代识别高质量数据以提升模型性能，尤其针对低资源多语言场景下的抽取式问答任务。我们的框架在MLQA数据集上，对印地语（F1/完全匹配EM）分别比机器翻译增强模型高0.22/1.68个点，对西班牙语高0.82/1.37个点；且在同一数据集上，比仅用英语数据集训练的模型在印地语上高5.05/6.50个F1/EM点，在西班牙语上高3.81/3.69个F1/EM点。值得注意的是，该方法使用预训练LLM进行生成而无需微调（FT），仅需ICL中一个标注示例即可生成数据，实现了成本效益高的开发流程。