Best practices to scale human data annotation for large datasets

Large language models (LLMs) are evolving rapidly. Trained on massive datasets, they are becoming increasingly sophisticated, pushing the boundaries of language understanding and generation. Emerging gen AI trends also reveal a growing interest in domain-specific models, fine-tuned for specialized tasks and domains, and multimodal models trained on diverse data types.

In this scenario, the need for high-quality training data — the fuel that enables AI models to learn — intensifies. Human judgment and expertise are more crucial now than ever, but a new challenge arises: How to scale human annotation efforts?

This article unveils best practices to effectively scale human data annotation while maintaining quality, ensuring consistency, and adapting to time constraints.

The data dilemma: How much training data is enough for LLMs?

Among the many challenges of training LLMs is the demand for gigantic amounts of training data. The exact volume varies based on the model’s intended use case and the complexities of the language domain. To determine the optimal dataset size, experts recommend experimenting with different data scales, such as 1,000, 10,000, and 100,000+ examples.

In the same vein, fine-tuning LLMs for specific domains, such as legal or medical fields, might require massive datasets of around 50,000 to 200,000 examples to achieve peak performance.

To tackle these immense data requirements, companies need to invest in strong data annotation processes, automation tools, and highly specialized teams.

The challenges of scaling human data annotation

Data annotation is the process of labeling raw, unstructured data with meaningful tags and information that AI models can understand. Human annotators play a vital role during the annotation and validation stages, ensuring the data is accurate, consistent, and relevant to the specific AI task.

However, scaling human data annotation for large AI datasets — like the ones needed to train LLMs and domain-specific models — presents a series of unique challenges:

Maintaining consistency

Annotators might have subjective interpretations of data, influenced by their backgrounds, cultural contexts, and personal experiences. In some cases, they might follow inconsistent labeling criteria. Additionally, human biases can perpetuate stereotypes, which can degrade the fairness, ethics, and representativeness of AI models.

Managing a large workforce of annotators

Finding and hiring hundreds of skilled data annotators, particularly those with specialized domain expertise, is a complex task. Once vetted and hired, they must be provided with a strong onboarding process and ongoing training, to ensure consistency and effective communication. Generative AI demands new skills from human annotators, such as creativity and judgment to interpret nuance, so it might also be necessary to upskill your workforce to meet the requirements of new annotation tasks. Finally, optimizing the annotation workflow is crucial to ensure efficient coordination and maximize productivity.

Ensuring quality across vast datasets

Scaling human data annotation involves implementing effective quality control mechanisms to ensure the highest data quality.

It’s important to address inconsistencies and errors early on, to avoid spreading them through your dataset. Errors can lead to significant problems down the line, such as biased models or inaccurate predictions. Remember, generative AI and LLMs amplify the data they are trained on. That’s why it’s crucial to ensure that training data is accurate, diverse, representative, and free from bias.

Handling time constraints

Balancing speed and accuracy is one of the biggest challenges when scaling data annotation processes. Companies need to be able to process large volumes of data while adapting to changing requirements and evolving project timelines. Bringing automation tools to the table is a strategic way to achieve annotation goals faster. However, it’s vital to carefully balance automation with a human-in-the-loop approach to avoid quality issues during the annotation process and to validate predictions.

Addressing data security and privacy

The annotation process often involves using sensitive data, such as medical records or financial information. Protecting this data from unauthorized access and breaches is key, as well as ensuring compliance with relevant data protection regulations, such as GDPR and HIPAA.

Best practices for managing large-scale annotation projects

Now that we’ve outlined the main challenges that companies face when scaling data annotation, let’s shift our focus to practical solutions.

How can you address annotation challenges and succeed at generating high-quality data while keeping humans at the center of the process?

Drawing from years of experience working with large-scale annotation teams and providing high-quality training data for some of the world’s biggest companies, here are Sigma’s best practices to consider:

Finding qualified candidates for each project

Every AI project presents unique data annotation challenges. A rigorous selection and hiring process is key to finding candidates with the right skills, availability, and experience.

“Quality assurance begins with our staffing selection process,” explains Valentina Vendola, Manager at Sigma. “Unlike traditional staffing or Business Process Outsourcing firms, we have developed specialized assessments to identify the exact skills required for each project. Our research has proven that this approach produces a higher level of quality from the start.”

With a pool of over 25,000 vetted and trusted annotators, Sigma can assemble teams with the necessary expertise, cultural sensitivity, and diversity to provide the best results.

Develop an effective onboarding plan

Once you’ve identified highly skilled annotators, take time to onboard them properly before jumping into a project. An onboarding program ensures annotators understand the purpose of their work, learn how to use annotation guidelines, and master annotation tools.

Consider this onboarding process:

Provide engaging online courses, videos, and interactive simulations to develop your workforce’s skills.
Communicate the importance of quality standards. For Sigma, this involves reinforcing the company’s values, its ethical code of conduct, key privacy and confidentiality requirements including GDPR and client needs, and security expectations.
Implement practical exercises with real-world data to evaluate annotator performance and identify knowledge gaps. Make sure to provide continuous and personalized feedback. “Depending on the complexity of the task, it can take up to two months for a worker to be fully trained,” explains Vendola.
Provide ongoing assistance and be available to address any questions or concerns through specific communication channels, such as chat forums, email support, and regular team meetings.

Creating clear data annotation guidelines

Developing strong data annotation guidelines is the best approach to ensure consistency and maintain quality in the data annotation process.

Guidelines should serve as a roadmap for annotators, enabling them to perform tasks efficiently and accurately.

To be truly effective, data annotation guidelines should:

Define the specific annotation tasks
Establish data labeling criteria, providing clear instructions and examples (including examples of both accurate and inaccurate labels)
Set quality standards, establishing metrics to measure performance, and explaining the level of accuracy and precision that is required
Address edge cases
Provide feedback mechanisms such as implementing a feedback loop to address annotators’ questions and concerns. Annotators should cultivate a critical eye and be capable of identifying ambiguous or confusing instructions.

Data annotation guidelines should be regularly reviewed and refined. In fact, they should be considered living documents, rather than being set in stone.

Establish quality control mechanisms

Teams must implement robust quality control mechanisms to ensure annotated data is accurate and reliable. Key quality assurance techniques include:

Creating a golden dataset — This involves creating a high-quality dataset that is used as a reference or benchmark for evaluating the accuracy and consistency of annotation. This helps maintain a standard across multiple types or groups of annotations.
Random sampling and manual review — This consists of randomly selecting a subset of annotated data and reviewing it manually.
Inter-annotator agreement (IAA) — This measures the consistency of responses from different annotators by comparing their annotations for the same data. This allows you to see to what extent annotators agree and helps identify discrepancies.
Elaborating on quality reports — Generate detailed reports that track key quality metrics, such as error rates, inter-annotator agreement scores, and the effectiveness of quality control measures (this can vary depending on the characteristics of the project).

Implement project management techniques to scale effectively

Managing a team of hundreds of annotators working on one or more annotation projects is challenging, as the complexity increases exponentially. Project management techniques are essential at this stage, helping to streamline the data annotation workflow and ensure a successful outcome.

These are some of the key strategies to consider:

Phased rollouts — Break down large projects into smaller, manageable phases. This will help you scale gradually while testing and refining the annotation process.
Pilot programs — Conducting a pilot project with smaller datasets allows you to test different approaches, identify potential bottlenecks, and learn lessons to improve and refine your annotation process.
Iterative improvements — Constantly monitoring and supervising human data annotation helps you identify areas for improvement and implement changes early, before they escalate into larger issues.
Clear communication channels — Define clear and efficient communication channels to collaborate and share information among team members. “Frequent team meetings are essential to provide feedback, share information, and ensure project alignment,” explains Vendola. This also includes continuous communication with the client to solve any issues that may arise and ensure the work is being performed as specified and the guidelines are clearly understood and applied.
Rotate annotators on different types of projects — This allows them to acquire new skills.

Bring technology to support scaling efforts

While humans are indispensable in the data annotation process, advanced AI-powered tools and platforms can accelerate certain tasks and improve efficiency.

By strategically integrating technology, teams can scale their annotation efforts without compromising quality.

Here are some examples of technology in data annotation:

AI-assisted annotation software — Manually labeling images can be extremely time-consuming. These tools can automate repetitive tasks, such as object detection and image segmentation, reducing the manual effort required from annotators and speeding up image data annotation.

Data anonymization tools — These tools are crucial for protecting sensitive information and ensuring privacy compliance. They can be used to remove personal data from a dataset before it is shared with annotators.
Data preprocessing tools — Preprocessing tools can help clean and prepare data for annotation, such as removing noise, normalizing text, or augmenting images.
Automated quality assurance techniques — Implement automated quality assurance techniques to identify potential issues early on, such as consistency checks, statistical analysis, and error detection algorithms. This should always be combined with manual quality assurance processes.

To learn more about data annotation at scale, read the case study about how Sigma successfully scaled video transcription in 24 languages and dialects.

How Sigma can help you scale human data annotation

Building the AI of the future requires massive amounts of high-quality training data — carefully curated, annotated, and validated by a team of expert human annotators.

But that’s not always easy. It takes time, expertise, and refined processes.

Sigma empowers companies to overcome data annotation challenges and fast-track their AI journey. We combine three key elements to deliver high-quality training data at scale:

Carefully designed processes: Our team of experts designs and optimizes data annotation workflows, ensuring consistency and efficiency across your projects.
Expert, fully trained annotators: We’ve built a dedicated team of over 25,000 skilled annotators with the necessary domain expertise to tackle complex annotation tasks.
Advanced tools: We leverage cutting-edge AI-powered tools to automate repetitive tasks, accelerate annotation processes, and improve quality control.

Ready to unlock the power of AI with high-quality training data? Contact us today to learn more or ask about a proof of concept.

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Sigma offers tailor-made solutions for data teams annotating large volumes of training data.