Description Guided Zero-Shot Labeling for NLP Applications

Using LLM

Walid Amamou

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Photo by Christopher Burns on Unsplash

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.

In this article, we explore the challenges posed by complex entities, and demonstrate how the inclusion of label descriptions can be a game-changer. We will examine the mechanics of label description guided auto-labeling, present real-world case studies and experiments, discuss its potential applications across industries, and explore the challenges that lie ahead on the road to achieving enhanced accuracy.

Setting up Zero-Shot Labeling

In this section, we will walk through the process of enabling description-guided auto-labeling using UbiAI, a powerful labeling platform designed to streamline the labeling process and model fine-tuning. We’ll illustrate this tutorial with practical examples from litigation case analysis.

For this tutorial, we are going to identify plaintiff, defendants and their claims from litigation cases using zero shot labeling. First we upload the document to UbiAI, below is a small snippet of the document:

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Walid Amamou

Founder of UBIAI, annotation tool for NLP applications| PhD in Physics.