Supervised learning relies on data annotation which usually is time-consuming and therefore expensive. A longstanding strategy to reduce annotation costs is active learning, an iterative process, in which a human annotates only data instances deemed informative by a model. Research in active learning has made considerable progress, especially with the rise of large language models (LLMs). However, we still know little about how these remarkable advances have translated into real-world applications, or contributed to removing key barriers to active learning adoption. To fill in this gap, we conduct an online survey in the NLP community to collect previously intangible insights on current implementation practices, common obstacles in application, and future prospects in active learning. We also reassess the perceived relevance of data annotation and active learning as fundamental assumptions. Our findings show that data annotation is expected to remain important and active learning to stay highly relevant while benefiting from LLMs. Consistent with a community survey from over 15 years ago, three key challenges yet persist—setup complexity, uncertain cost reduction, and tooling—for which we propose alleviation strategies. We publish an anonymized version of the dataset.

In supervised learning, a significant amount of data is essential. To achieve this, we generated and evaluated datasets based on a provided dataset using transformer and non-transformer models. By utilizing these generated datasets during the training of new models, we attain a higher balanced accuracy during validation compared to using only the original dataset.

This paper describes an approach to automat- ically close the knowledge gap of Clickbait- Posts via a transformer model trained for Question-Answering, augmented by a task- specific post-processing step. This was part of the SemEval 2023 Clickbait shared task (Frbe et al., 2023a) - specifically task 2. We devised strategies to improve the existing model to fit the task better, e.g. with different special mod- els and a post-processor tailored to different inherent challenges of the task. Furthermore, we explored the possibility of expanding the original training data by using strategies from Heuristic Labeling and Semi-Supervised Learn- ing. With those adjustments, we were able to improve the baseline by 9.8 percentage points to a BLEU-4 score of 48.0%.