Hands-On Tutorial: Labeling with LLM and Human-in-the-Loop

• URL: http://arxiv.org/abs/2411.04637v1
• Date: Thu, 07 Nov 2024 11:51:14 GMT
• Title: Hands-On Tutorial: Labeling with LLM and Human-in-the-Loop
• Authors: Ekaterina Artemova, Akim Tsvigun, Dominik Schlechtweg, Natalia Fedorova, Sergei Tilga, Boris Obmoroshev,
• Abstract summary: This tutorial is designed for NLP practitioners from both research and industry backgrounds. We will present the basics of each strategy, highlight their benefits and limitations, and discuss in detail real-life case studies. The tutorial includes a hands-on workshop, where attendees will be guided in implementing a hybrid annotation setup.
• Score: 7.925650087629884
• License:
• Abstract: Training and deploying machine learning models relies on a large amount of human-annotated data. As human labeling becomes increasingly expensive and time-consuming, recent research has developed multiple strategies to speed up annotation and reduce costs and human workload: generating synthetic training data, active learning, and hybrid labeling. This tutorial is oriented toward practical applications: we will present the basics of each strategy, highlight their benefits and limitations, and discuss in detail real-life case studies. Additionally, we will walk through best practices for managing human annotators and controlling the quality of the final dataset. The tutorial includes a hands-on workshop, where attendees will be guided in implementing a hybrid annotation setup. This tutorial is designed for NLP practitioners from both research and industry backgrounds who are involved in or interested in optimizing data labeling projects.

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