It’s crucial to balance accuracy and efficiency when labeling datasets for machine learning—especially when LLMs are involved. In this article we explore a variety of techniques and assess the optimal labeling methods for different projects.

When developing machine learning (ML) models, the quality and granularity of labeled data have a direct impact on performance. Labeling methods encompass a wide range of techniques, from fully manual, in which subject matter experts (SMEs) label all data by hand, to fully automated, in which software tools algorithmically apply labels. Manual labeling generally yields the highest quality results but can be time-consuming and expensive, whereas automated labeling may be faster and more efficient, but often at the cost of accuracy or granularity.

In practice, hybrid approaches—combining manual and automated techniques throughout the process—are generally considered to be the most effective. And with the rise in popularity and accessibility of large language models (LLMs), there are an increasing number of ways in which software can augment and accelerate the work of human annotators. Nonetheless, it’s important to understand where and when the necessity for human involvement persists.

This article examines a variety of advanced data labeling methods, exploring their real-world applications and use cases. We consider the strengths and limitations of each technique across different modalities, such as text, images, videos, and audio data, and offer guidance for selecting the most appropriate techniques based on project-specific requirements.

When developing machine learning (ML) models, the quality and granularity of labeled data have a direct impact on performance. Labeling methods encompass a wide range of techniques, from fully manual, in which subject matter experts (SMEs) label all data by hand, to fully automated, in which software tools algorithmically apply labels. Manual labeling generally yields the highest quality results but can be time-consuming and expensive, whereas automated labeling may be faster and more efficient, but often at the cost of accuracy or granularity.

In practice, hybrid approaches—combining manual and automated techniques throughout the process—are generally considered to be the most effective. And with the rise in popularity and accessibility of large language models (LLMs), there are an increasing number of ways in which software can augment and accelerate the work of human annotators. Nonetheless, it’s important to understand where and when the necessity for human involvement persists.

This article examines a variety of advanced data labeling methods, exploring their real-world applications and use cases. We consider the strengths and limitations of each technique across different modalities, such as text, images, videos, and audio data, and offer guidance for selecting the most appropriate techniques based on project-specific requirements.