What can transfer learning reduce in NLP tasks?

Transfer learning is a technique in machine learning that allows a model trained on one task to be adapted for another task. In the realm of natural language processing (NLP), this approach has shown significant benefits, particularly in reducing the need for large labeled datasets.

When employing transfer learning, models, such as those based on transformer architectures, can leverage knowledge gained from large-scale datasets (like those used for pre-training) to perform well on specific tasks with relatively smaller amounts of labeled data. This is especially important in NLP, where acquiring labeled datasets can be costly and time-consuming. Rather than starting from scratch, a model can be fine-tuned on a smaller dataset, effectively learning the nuances of the new task while benefiting from the general language understanding gained during pre-training.

The other options focus on different aspects that are not directly addressed by transfer learning. Reducing the complexity of algorithms is not a primary focus, as transfer learning enables the use of sophisticated models rather than simplifying them. Clarity of the text is related to language processing but is not influenced by the transfer learning process itself. Similarly, while transfer learning can lead to quicker training times, it does not inherently reduce the need for human intervention in the task of data annotation or model management.