# Uncertainty-Aware Active Meta-Learning for Few-Shot Text Classification
## 6 Conclusions  
The issue of low-resource natural language understanding (NLU) presents a significant obstacle in the field of artificial intelligence, where the vast majority of models require large volumes of annotated data to perform well. However, such datasets are scarce or non-existent for many languages and specialized domains. This scarcity poses a challenge for developing robust NLU systems that can interpret and act upon human language with the same level of comprehension across diverse contexts. Based on this awareness, this research proposes Uncertainty Aware Active Meta-Learning (UA-AML), a methodology that enables learning from informative data using uncertainty. It is shown through the experiments that the proposed methodology was applied to few-shot relation classification and few-shot sentiment classification, which are representative problems set as class-as-task and domain-as-task. As a result of such research, this study shows that the proposed meta-learning methodology can be effectively utilized in more diverse application fields by extending meta-learning research, mainly computer vision, to the field of natural language processing. In the future, it is necessary to research meta-learning methodologies suitable for diverse domain characteristics in natural language processing. The findings suggest that UA-AML is a valuable addition to the toolkit of meta-learning strategies, extending its use beyond the realm of computer vision into natural language processing. This approach shows promise for a range of NLU applications, particularly where data are scarce. The research opens up new possibilities for future exploration in meta-learning for natural language understanding, indicating that UA-AML could be effectively adapted to various other NLU tasks with limited data resources. While UA-AML introduces an efficient uncertainty-aware task selection framework, its deployment in real-world applications requires careful attention to ethical considerations, particularly regarding bias, fairness, and data sensitivity in low-resource settings. Since the model prioritizes high-uncertainty tasks, there is a potential risk that it may unintentionally amplify biases present in the dataset, as underrepresented or systematically mislabeled data may receive disproportionate weight during training. This could lead to biased model predictions, particularly in critical domains such as healthcare, finance, and legal AI applications. There needs to be further research exploring bias-aware uncertainty estimation techniques and fair task selection strategies to ensure that active learning pri  
Figure 5: Uncertainty Calibration Plots by Uncertainty (UAR) Applying Rate per Epoch comparing SNGP (**left**) and UA-AML + SNGP with Uncertainty Applying Rate: 0.8 (**right**). The blue line represents the model’s actual predictive accuracy at each predicted probability level, while the dashed black line indicates perfect calibration. -oritizes diverse and representative data without reinforcing existing biases. Furthermore, integrating explainability mechanisms to provide transparency into how uncertainty affects task selection can help mitigate ethical concerns and enhance trust in model predictions. Future work could also explore alternative uncertainty quantification methods, such as Deep Ensembles, to assess whether they provide similar improvements as Monte Carlo Dropout while balancing computational efficiency and performance gains. Additionally, optimizing the computational efficiency of uncertainty estimation and active learning remains an important challenge, as the current approach introduces a computational overhead. Future research could focus on developing lightweight uncertainty estimation techniques or adaptive task selection strategies to improve efficiency while maintaining the benefits of uncertainty-aware learning.