Applying Deep Active Learning (DAL) to generative tasks, such as summarization and question answering, presents both challenges and opportunities compared to classification tasks:
Challenges:
Complexity of Generative Tasks: Generative tasks involve creating new content, which requires a deeper understanding of the data and context compared to classification tasks. Selecting the most informative samples for generative tasks can be more challenging as the criteria for informativeness may vary.
Evaluation Metrics: Evaluating the quality of generated content in generative tasks is subjective and often requires human judgment. This makes it difficult to define clear criteria for selecting informative samples in DAL for generative tasks.
Data Scarcity: Generative tasks often require a large amount of labeled data to capture the nuances and complexities of the task. Limited labeled data can hinder the effectiveness of DAL in generative tasks.
Opportunities:
Improved Content Generation: By effectively selecting informative samples through DAL, models for generative tasks can be trained on high-quality data, leading to improved content generation and higher-quality outputs.
Reduced Annotation Efforts: DAL can help reduce the need for extensive human annotation in generative tasks by selecting the most valuable samples for annotation. This can significantly lower the annotation costs and time required for training generative models.
Enhanced Model Performance: By leveraging the benefits of active learning in generative tasks, models can learn more efficiently from limited labeled data, leading to enhanced performance and better generalization to new data.
Overall, while there are challenges in applying DAL to generative tasks, there are also significant opportunities to improve content generation, reduce annotation efforts, and enhance model performance in these tasks.