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

2024-11-07

arXiv.org

AI Summary

High annotation costs and prolonged turnaround times plague NLP development, necessitating efficient and reliable data labeling paradigms. This paper proposes an LLM-powered Human-in-the-Loop (HITL) hybrid annotation framework that systematically integrates synthetic data generation, active learning, and human-AI collaboration, augmented with built-in mechanisms for annotation quality assessment, annotator management, and cost-benefit analysis. Unlike prior work—largely theoretical or narrowly scoped—this study introduces the first deployable, plug-and-play industrial-grade annotation methodology, bridging the critical gap between methodological research and real-world engineering practice. Empirical validation across multiple production NLP projects demonstrates that the framework consistently reduces annotation costs and cycle time by 30–50%, while maintaining label quality within required thresholds.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Humans and AI: Human-in-the-loop Machine Learning
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

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.

Problem

• Machine Learning
• Natural Language Processing
• Annotation Cost

Innovation

• Active Learning
• Hybrid Labeling
• Computer-Generated Training Data