The field of Natural Language Processing (NLP) has seen significant advancements in recent years. Previously, supervised learning techniques were commonly used with large datasets and few classes. However, these techniques have become less popular as unsupervised learning methods have become more prominent. These methods often require large amounts of compute power and data, making them more difficult to implement. In addition, the focus of NLP research has shifted from creating new network architectures for specific tasks to improving the efficiency and scalability of existing models. As a result, the field of NLP has become more focused on engineering and less on inventing new architectures, leading some researchers to view it as less exciting than it used to be.
LSTMs are out, large supervised datasets with few classes for each task are out, architectural hacking is out. Now we got prompting.
visarga|3 years ago
LSTMs are out, large supervised datasets with few classes for each task are out, architectural hacking is out. Now we got prompting.