Description Guided Zero-Shot Labeling for NLP Applications
Using LLM
Zero-shot Labeling using LLM such as GPT is a promising approach to quickly create training data with minimal human input. It enables training AI systems without needing to manually label the entire dataset. However, one of the disadvtanage of this approach is accurate classification of complex and ambiguous entities.
Imagine a scenario where an AI system needs to label entities in news articles. While classifying straightforward topics like “sports” or “politics” might be a breeze, things get tricky when we encounter more intricate entities like “artificial intelligence regulations,” “climate change agreements,” or “financial market fluctuations.” These labels often carry inherent ambiguity, and traditional auto-labeling systems may stumble when trying to disentangle the subtle nuances that differentiate one label from another.
This is where the concept of “Description guided zero-shot labeling” enters the scene. By providing concise and informative descriptions for each label, we equip our LLM with invaluable context and clarity. This approach holds the promise of significantly enhancing the accuracy of zero-shot auto-labeling by offering guidance and disambiguation precisely when it’s needed most.
