# Uncertainty-Aware Active Meta-Learning for Few-Shot Text Classification
## 1 Introduction  
The field of natural language understanding (NLU), a key component of artificial intelligence, has seen substantial progress in recent years. Low-resource natural language understanding (NLU) involves developing models and systems that can process and understand human language in scenarios where available data are limited. This area is particularly crucial for languages and tasks that do not have large annotated datasets like English. However, developing learning techniques for low-resource NLU models remains a challenging task [1, 2, 3]. Low-resource NLU and few-shot text classification are closely related to enabling models to handle linguistic tasks in data-scarce environments. In low-resource NLU,models must be able to understand and process language with limited data across various languages or specific domains. Key tasks in NLU include intent classification, named entity recognition (NER), sentiment analysis, machine translation, and question answering, all demanding the ability to comprehend different linguistic expressions and analyze contexts. Few-shot text classification trains models to perform classification tasks with only a few examples, maximizing the ability of models to quickly adapt to data-scarce languages or tasks. Although large language models (LLMs) have demonstrated excellent performance through instruct tuning, the use of smaller-scale models remains critical in few-shot text classification due to resource efficiency and practicality. Large models come with high computational costs and significant memory and power consumption, making them impractical in real-world applications. Particularly in applications with limited hardware resources or where real-time processing is required, resource-efficient models are more suitable. Moreover, few-shot learning techniques that enable efficient performance with small data quantities are essential. Therefore, the few-shot text classification task remains a significant issue in the low-resource NLU area, and the development of resource-efficient models is crucial from both practical and economic perspectives. This research focuses on the concept of meta-learning, a process of learning how to learn, which becomes particularly crucial when the amount of data available for a task is limited. Traditional machine learning models typically require substantial data to train effectively, but many real-world scenarios only provide small data quantities. Meta-learning techniques are designed to learn from small data quantities and generalize tasks efficiently [4; 5], addressing the increasingly common issue of few-shot learning problems in machine learning. In few-shot learning problems, models must make accurate predictions based on limited data, a task at which traditional models often struggle. However, this research also recognizes the limitations of existing meta-learning methodologies. Many such methodologies focus primarily on computer vision, leaving a gap in natural language processing. Computer vision and natural language processing are fundamentally different domains, each with distinct data types and challenges. There is a relative scarcity of research considering the domain-specific characteristics of natural language processing, and this research aims to bridge this gap. Moreover, the problem of meta-overfitting in the meta-learning process is identified as a major cause of performance degradation in few-shot learning models [6; 7; 8]. Overfitting occurs when a model learns the training data too well, degrading its performance on unseen new data. This is a common issue in machine learning, particularly challenging within the context of meta-learning. Meta-learning models need to generalize well from small data quantities but are susceptible to high risks of overfitting. In response to these challenges, this research proposes new meta-learning techniques for building efficient generalization models for natural language understanding tasks using limited resource learning data.