As large language models (LLM) advance in the understanding and generating of human-like text, it is important to balance efficiency and accuracy in labeling. According to industry surveys, companies spend an average of 80% of their AI project time on data preparation and labeling, highlighting the critical nature of this process. Data labeling provides LLMs with an organized framework to accurately interpret enormous text datasets, with the global data labeling market expected to reach $8.2 billion by 2027.

Therefore, tailoring some techniques to the specific requirements of LLM training will make them stand out. Let’s explore the concept of data labeling, the role of LLMs in enhancing its efficiency, and the best data labeling techniques for creating cutting-edge language models.

What Is Data Labeling?

Data labeling is the categorization and tagging of data used to train machine-learning models. For the LLMs, data labeling within most models typically consists of labeling text data to help models understand things such as context, emotional assessments, relationships, or different subtleties of language.

In the case of a customer service scenario at Amazon, when processing millions of customer reviews, data labeling helps identify not just whether a review is positive or negative but also specific product issues, shipping concerns, or service quality mentions. For example, we would label a review that states “The product arrived late but the quality exceeded my expectations” as both negative for delivery timing and positive for product satisfaction.

By adding labels to the dataset, the developers establish the foundation for learning within LLMs, empowering them to respond to specific inputs with maximum effectiveness. Labels can indicate whether the text is positive or negative, identify specific linguistic phrases, or even define the relationships between entities, such as a person and a location within a text.

Thus, data labeling provides those necessary knowledge frameworks that enable an LLM to start “understanding” text at scale. High-quality data labeling will ensure that these models perform well in real-world applications like customer service bots or content-generating tools.

Various Data Labeling Techniques to Adopt for LLMs

Adopting data labeling methods for LLMs varies along lines defined by complexity, data volume, and the nature of the data. This section discusses several notable strategies for labeling data to create strong language models.

#1 Manual Labeling

The value of manual labeling never really fades over time, despite its tedious and time-intensive methods. It is particularly useful for areas where human judgment is a determining factor. This technique involves the examination and manual labeling of data by skilled annotators, who ensure the accurate marking of certain nuances in language, context, and sentiment. We generally apply this labeling method at the onset of training LLMs on newer datasets or highly domain-specific data.

For example, in healthcare, Memorial Sloan Kettering Cancer Center employs skilled medical professionals to manually label thousands of oncology reports to train their AI systems. Each document takes these annotators approximately 15 to 20 minutes to accurately label critical medical terminology and relationships.

Manual labeling is resource-intensive, but it also guarantees high precision and sets a baseline for validating the accuracy of the automated techniques later. This tool can be especially valuable for specialized applications like analyzing legal or medical texts, where the precision of the data label is a primary concern.

#2 Automated Labeling

Automated labeling systems have revolutionized high-volume data processing. Take Gmail’s spam detection system, which automatically labels millions of emails daily using machine learning algorithms. While processing roughly 100 million emails per day, Google’s automated labeling system achieves a reported accuracy rate of 99.9% in spam detection.

The goal of automated labeling, also known as machine-assisted labeling, is to process large amounts of data as quickly as possible. The automated systems, depending on some form of algorithm or pre-trained model for classification, can do this labeling autonomously without human guidance.

The primary benefit of automated data labeling is its speed; it can perform text and label a thousand pieces of data in a matter of minutes. Of course, this speed comes with a major limitation: the inability to manipulate certain data due to their intricate nature, which automated systems cannot label.

This means that automated labeling of LLMs will require validation to ensure their accuracy. Automated labeling is mostly useful for simple data such as basic sentiment tagging and language detection, where contextual precision is less important. It is also very well-suited for early training processes, enabling the initial developers of the model to create a broad, labeled dataset that they could refine afterwards through human review.

Netflix gives a classic example. It uses automated labeling to tag thousands of hours of content with genre information, scene descriptions, and emotional content markers. This powers their recommendation system, which serves 230+ million subscribers globally.

#3 Self-Supervised Learning for Data Labeling

GPT models demonstrate the power of self-supervised learning. During pre-training, these models analyze billions of web pages, books, and documents to understand language patterns without explicit labeling. For instance, Instagram uses self-supervised learning to automatically generate content warnings and age-appropriate content labels across its platform of over 2 billion monthly active users.

Self-supervised learning uses LLMs’ ability to recognize patterns and generate data labels by understanding some underlying structure inside the dataset. By gaining insights from unlabeled data, it enhances the model’s performance. This approach indirectly contributes to the reduction of manual supervision and associated costs. Self-supervised learning often generates complex high-level labels for large datasets to help eliminate substantial amounts of manual effort.

For instance, a self-supervised LLM would find a set of documents with common themes and entities and then formulate high-quality dataset labels based on these findings. It is an efficient way to produce good-quality dataset labels in a scalable and cost-effective manner. However, note that an LLM requires a substantial initial data set. Self-supervised learning relies on the model’s self-training to create precise labeling techniques.

#4 Human-in-the-Loop (HITL) Labeling

Content moderation at social media giants like Facebook exemplifies HITL labeling. Their system automatically flags potentially problematic content, which human moderators then review. In 2023, Meta reported that AI systems flagged 98% of hate speech content, but human reviewers were crucial in understanding context and making final decisions.

HITL is a hybrid technique that combines automated labeling with some human perspective. In the HITL model, automated labeling systems bear the majority of the workload, while human annotators verify the correct assignment of each label on a given dataset and correct errors as necessary. It narrows the gap that might exist between the human factor and the efficiency of automated data labeling.

HITL technology plays a crucial role, particularly when partial automation is possible but complex contextual understanding is required. In an example where an LLM is labeling customer complaints, the initial stage of automatic labeling will classify the data broadly based on a specific theme.

After that, human reviewers will take time to enrich these dataset labels with specific nuances or concerns. This promotes not only accuracy in labeled data but also hastens the arrival of sophisticated models, with human input relegated to portions where it is most indispensable.

#5 Weak supervision

Weak supervision involves making use of weakly associated data sources, such as noisy labels or partially labeled datasets for training models. This technique exposes the LLMs to a large number of dataset labels, many of which may not be accurate. The model learns by pulling patterns together from these imperfect labels, quite often converging at an accuracy sufficient to handle the dataset.

YouTube’s content categorization system employs weak supervision by using multiple noisy signals: user-generated tags, automatic speech recognition transcripts, and viewer behavior patterns. Despite individual signals being imperfect, the combination helps accurately categorize billions of videos.

When financial or time constraints prevent labeling large datasets with perfect accuracy, weak supervision becomes particularly useful. Weakly supervised training provides models with insights from various data sources, including user-provided tags and additional annotation metadata. This approach enables speedy scaling on the part of developers while they balance accuracy requirements.

#6 Transfer Learning for Data Labeling

Transfer learning is using pre-existing models trained on similar data to help in the labeling of new datasets. Using a model that has already entrenched knowledge about language, relationships, and context will enable LLMs to better assign dataset labels.

Transfer learning is of more benefit when tackling a specific case or particular data set. Why? The prelearned associations and patterns of the model enhance its adaptability to somewhat related new content.

As an example, a model that is trained using general business language might then be fine-tuned for labeling data in the field of law or finance. Transfer learning makes labeling extremely efficient and tailored. Since the model uses its prior knowledge to apply to the new data, it drastically decreases the extent to which manual oversight is required.

How We Can Help

North South Tech builds efficient data labeling solutions that work across manual, automated, and hybrid approaches. We recognize that excellent data labeling requires more than just checking boxes; it demands a deep understanding of how language models learn and adapt.

Drawing from years of experience with both automated systems and human-guided labeling, we’ve refined techniques that deliver high-quality training data while keeping costs manageable. When clients struggle with massive datasets or complex language requirements, we implement custom labeling workflows combining self-supervised learning with targeted human review.

This eliminates bottlenecks without sacrificing accuracy. We’re actively labeling millions of data points for companies developing next-generation AI models. Let’s discuss how thoughtful data labeling can strengthen your language models. Message us to schedule a technical consultation.