What is a golden dataset?
A golden dataset is a curated collection of human-labeled data that serves as a benchmark for evaluating the performance of AI and ML models, particularly fine-tuned large language models.
Because they are considered ground truth — the north star for correct answers — golden datasets must contain high-quality data that is:
- Accurate — Obtained from qualified sources and free from errors, inconsistencies, and inaccuracies.
- Complete — Covers all the aspects of the real-world phenomenon that the model intends to capture, including edge cases. The dataset should contain sufficient examples to evaluate the model effectively.
- Consistent — Organized in a uniform format and structure. Labels should be standardized to avoid ambiguities.
- Bias-free — The dataset should represent a diverse range of perspectives and points of view and avoid biases that could negatively impact the model’s performance.
- Timely — The data should be up-to-date and relevant to the domain’s current state. To ensure this, it should be regularly updated to reflect any changes in the real world.
Why is a golden dataset important for evaluating large language models?
Golden datasets are essential for evaluating fine-tuned LLMs, ensuring they meet high standards of quality and accuracy. Let’s explore some of the key reasons that make them indispensable:
- Establishing a baseline. A golden dataset provides a solid foundation for measuring the performance of LLMs. By comparing the model’s output to the human-verified ground truth, data scientists can assess a model’s accuracy, coherence, and relevance of its responses.
- Identifying biases and limitations. By evaluating the model against the golden dataset, data scientists can identify discrepancies, uncovering biases and limitations in the model. This information can be used to make the necessary adjustments to the model and improve its performance through an iterative process.
- Evaluating the performance of LLMs for specific tasks and domains. Golden datasets are crucial for evaluating fine-tuned LLMs, which have been trained to increase their performance and capabilities in specific domains like healthcare, legal, or finance, or tasks like medical diagnosis or fraud detection. Human annotators with subject matter expertise are often involved in annotating and validating these golden datasets. With this dataset as a reference, data scientists can assess the model’s performance, as well as its ability to understand and use domain-specific vocabulary.
What are the steps to creating a high-quality golden dataset?
Curating a golden dataset requires careful planning and demands a considerable amount of time and effort. Building on our expertise in providing high-quality human-annotated data at Sigma AI, we’ve outlined the key steps to create a golden dataset for LLMs evaluation:
Identify the main goal
Before anything else, answer this question: What do you want to achieve by creating a golden dataset?
“To create a truly effective golden dataset, it’s essential to have a clear vision,” explains Jean-Claude Junqua, Executive Senior Advisor at Sigma AI. “By defining the specific objective, whether it’s fine-tuning a model or conducting any other kind of evaluation, we can ensure the dataset aligns with our goals and delivers valuable insights.”
Collect data
Identify relevant data sources relevant to your model’s domain. This might include public datasets (such as those from government agencies, research institutions, or open-source communities), proprietary data, and web scraping.
Collect a diverse and representative dataset, covering multiple scenarios, perspectives, and edge cases. Ensure that your dataset has a balanced distribution of different classes or categories.
The number of examples needed to create a golden dataset will depend on the complexity of the task (for instance, tasks like medical diagnosis might require larger datasets), the desired level of accuracy, and the quality of the available data (high-quality data, free from noise and inconsistencies, can reduce the required dataset size).
Prepare the dataset
Clean the dataset to remove noise, inconsistencies, and errors. Normalize data to a uniform and consistent format such as JSON or CSV.
Leverage human expertise for data annotation
Develop clear data annotation guidelines to maintain consistency across the data annotation process. Involve a team of human annotators with domain expertise and diverse backgrounds to annotate data accurately.
Validate data
Implement quality control procedures to evaluate the accuracy of annotations. This may include cross-validation, involving external experts, and using statistical methods to review your annotated data. Conduct audits and apply fairness metrics to assess the model’s performance across different demographic groups and identify potential biases.
Maintain, refine, and update the golden dataset
Regularly revise and update the dataset to ensure it stays relevant. A golden dataset should be considered a living document. This means it should be subject to continuous refinement and improvement. As models evolve and new insights emerge, the dataset needs to be updated to reflect these changes. That way, data scientists can ensure it remains accurate, relevant, and effective.
The role of human annotators in creating golden datasets
Human annotators play a critical role in creating high-quality golden datasets.
Subject matter experts (SMEs), with their deep domain knowledge, can handle complex, highly specific data, and make nuanced decisions during the annotation process. They can interpret ambiguous data points and assign appropriate labels to ensure the dataset reflects real-world scenarios.
In medical image annotation, for example, radiologists can accurately identify and label abnormalities in X-rays, CT scans, and MRIs. Their expertise is key for training AI models to detect diseases like cancer, pneumonia, or cardiovascular conditions with high precision.
In finance, financial analysts can accurately label documents, such as tax returns, financial statements, and contracts. Golden datasets can then be used to train AI models for tasks like fraud detection, risk assessment, and financial forecasting.
SMEs can identify and correct errors, inconsistencies, and biases in the data. They can also handle edge cases and anomalies that tend to be more difficult for automated tools to process.
Involving human experts in the creation of golden datasets is the best way to ensure that the data is of the highest quality. Their valuable input and unique contextual understanding can contribute to ensuring LLMs are accurate, reliable, and ethical.
Getting started: Accelerate your AI journey with Sigma AI
Golden datasets provide an essential benchmark for evaluating the performance of fine-tuned large language models. By ensuring LLMs generate accurate and reliable outputs, they contribute to making them more effective across diverse applications.
However, the process of creating a golden dataset can be resource-intensive, demanding a large amount of time, effort, and expertise. That’s where Sigma AI can help. Our team of more than 25,000 annotators has the skills, continuous training, and subject matter expertise you need to tackle your AI and ML challenges, no matter how complex they are.
Optimize model performance with expert-curated golden datasets. Contact us to learn more about our data annotation services and how we can help you build trustworthy and ethical AI models.