# Uncertainty-Aware Active Meta-Learning for Few-Shot Text Classification
## 4 Methodology
### UA-AML: Uncertainty Aware Active Meta-Learning  
As depicted in Figure 2, the overall workflow of the proposed methodology is presented. The process begins with a support set and query set, where the model receives few-shot learning tasks. The base model processes the input data with the use of Monte Carlo Dropout (MC-Dropout) to estimate uncertainty by generating \(T\) stochastic forward passes. The uncertainty quantification module then calculates uncertainty values using entropy-based methods for classification tasks and variance-based methods for regression tasks. These uncertainty values are then normalized and used to define sample importance, ensuring that the model focuses more on hard samples (high uncertainty) while stillincorporating easier samples (low uncertainty, high confidence) for stable learning. The Uncertainty-Based Sample-Balanced (USB) loss further integrates these uncertainty values into the training process, re-weighting the contribution of each sample dynamically. The model is then updated using gradients derived from the USB loss, reinforcing generalization across diverse few-shot tasks. This approach differs from traditional active learning by not requiring explicit sample selection but instead adjusting sample importance weights during training. By using the data of the task possessed in the meta-learning process, the output value is stored by repeatedly inputting it \(T\) times to the same base model in which the dropout layer is activated. For the \(T\) output values, in the case of a classification model using cross entropy, uncertainty is quantified using the entropy of the output distribution, and variance is quantified as uncertainty when MSE, which calculates the distance, is used as a loss function. Each uncertainty is mapped to a value in the range \(\{0,\ 1\}\) through min-max normalization. At this time, uncertainty is used as a value applied to samples that are difficult for the model to learn, and confidence value is defined as \(confidence=1-uncertainty\) for easy samples to support stable learning of the model. In this research, based on the approach of existing studies, the uncertainty of the model is quantified with respect to the input data, and the loss function is designed so that the influence of the more informative sample is reflected more when the model is trained. Through this, the model performs training mainly on difficult samples with high uncertainty for various tasks it has so that a more generalized meta-learning model can be built. Unlike previous studies, our proposed method quantifies the prediction uncertainty of the model for the given data so that the uncertainty is reflected in the learning process or, in our case, the meta-learning process. Summarizing the entire framework of the meta-learning process, the uncertainty of the input data is quantified to quantitatively distinguish the data that the model judges to be uncertain from the data that the model judges to be certain. This allows the model to learn mainly from samples that are difficult to classify and supports the generalization of the model by providing a more severe environment as a constraint. However, the model may be underfitted if only difficult samples are continuously provided as input. Therefore, it is essential to include a certain percentage of easy samples in the training data to ensure stable learning of the model [30,32]. Existing active learning is a method to label useful data, but in this research, learning is performed by giving weight to valuable data, and if no weight is given, learning is not affected at all. Figure 2: Uncertainty Aware Active Meta-Learning (UA-AML) framework. Next, this paper presents a method of applying uncertainty and confidence to the loss function. The loss function is regarded as a sample-balanced loss that re-weights the influence of given samples based on uncertainty and Uncertainty-Based Sample-Balanced (USB) loss. The gradients are obtained from the USB loss, and the base model is trained as a final step.