What is a Golden Dataset?

by Stephen M. Walker II, Co-Founder / CEO

What is a golden dataset?

A golden dataset in the context of Large Language Models (LLMs) refers to a high-quality, hand-labeled dataset that is used for training and evaluating the performance of these models. This dataset is often considered the "ground truth" and is used to measure the performance of the LLMs.

Creating a golden dataset is a labor-intensive process, often requiring the input of subject matter experts (SMEs) to ensure the accuracy and relevance of the data. The dataset typically consists of question-answer pairs that closely match actual user scenarios. Platforms like Klu.ai accelerate the assembly of a golden dataset by enabling AI Teams to use real-world question-answer pairs for future testing.

However, due to the cost and effort involved in creating a golden dataset, there are also approaches that leverage automated golden dataset creation for accurate and automated assessment. In some cases, an auto-generated "silver" dataset can be used to guide the development and initial retrieval of LLMs. Many teams we work with start by bootstrapping answers from a superior frontier model (eg. GPT-4) and then evolving the dataset over time.

Klu.ai streamlines the creation of golden datasets by providing tools for teams to aggregate usage data and then filter by analyzing system performance, user preferences, or online evaluations. The platform simplifies the process of labeling data, which can then be directly utilized to fine-tune custom models.

The golden dataset is crucial in the evaluation process of LLMs. It helps establish a benchmark for the LLM evaluation metric. The optimized performance on the golden dataset represents how confident you can be on your LLM evaluation.

It's important to note that while a golden dataset provides a high standard for evaluation, the performance of an LLM on this dataset is only as accurate as its relationship to real-world prompts. However, it is generally more accurate and costs much less than having a human labeler involved in every example.

How can you bootstrap an initial golden dataset using generative data from an LLM?

Bootstrapping an initial golden dataset using generative data from an LLM can be a practical starting point for model evaluation. This approach leverages the LLM's ability to generate diverse and complex data samples. Accuracy and comprehensiveness of the dataset are critical for reliable evaluation.

To bootstrap an initial golden dataset from an LLM:

  1. Generate Synthetic Data — Use the LLM to create synthetic data when actual data is scarce.

  2. Collect Real Logs — Gather production logs with a tool like Klu.ai. Ensure the dataset reflects diverse user interactions.

  3. Refine Dataset — Adjust the dataset by mitigating biases, enhancing underrepresented data, and adding diverse examples.

  4. Evaluate Quality — Use predefined standards to evaluate the synthetic data's quality.

  5. Iterative Improvement — Employ self-training techniques to improve the LLM iteratively using synthetic and real data.

  6. In-Context Learning — Apply few-shot learning to adapt the model to new contexts with example Q&A pairs.

What is the purpose of a golden dataset?

For evaluating large language models (LLMs), a golden dataset comprises a set of well-crafted questions and answers or other pertinent data that gauges the performance of an LLM. It acts as a standard to measure the model's outputs against expected results, thus evaluating the model's accuracy and response correctness.

Golden datasets are essential for several reasons:

  1. They provide a single source of truth, ensuring that different teams and applications within an organization are working with the same, reliable data.
  2. They enable consistent and accurate data analysis, reporting, and decision-making.
  3. In LLM evaluation, they help measure the model's performance and identify areas for improvement by comparing generated outputs to the high-quality reference data.

Golden datasets are integral to data governance and quality management, underpinning data-driven decision-making in businesses. In AI development, particularly for LLMs, they provide a benchmark for model performance evaluation.

What are the benefits of using a golden dataset for LLM evaluation?

Evaluating large language models (LLMs) with a golden dataset offers a dependable benchmark, ensuring accuracy and precision in model responses by comparing them to high-quality, human-validated question-answer pairs. This dataset serves as a "ground truth" for performance measurement, tailored to specific domains, and provides a cost-effective alternative to extensive human labeling, while maintaining data quality and establishing performance benchmarks.

  • Accuracy and Precision — A golden dataset consists of question-answer pairs that closely match actual user scenarios, providing a high-quality benchmark for assessing the performance of an LLM. This can help ensure that the model's responses are accurate and precise.

  • Ground Truth Labeling — The golden dataset provides "ground truth" labels, which are often derived from human feedback. These labels serve as a standard against which the LLM's performance can be measured.

  • Domain-Specific Evaluation — Golden datasets can be tailored to specific domains, making them particularly useful for evaluating LLMs in highly technical settings.

  • Cost-Effective Evaluation — While creating a golden dataset can be labor-intensive, it can be more cost-effective than having a human labeler involved in every example. Automated processes can also be used to generate question-answer pairs, reducing the time and effort required.

  • Quality Control — The use of a golden dataset can help maintain high data quality, which is crucial for the effectiveness of LLMs.

  • Benchmarking — A golden dataset can be used to establish a benchmark for LLM evaluation metrics, which can then be used to assess the performance of an LLM application.

While golden datasets are valuable for evaluating LLMs, they come with challenges. Without using a platform like Klu.ai, the creation and upkeep of these datasets can be expensive, particularly for intricate domains. Moreover, the evaluation's quality is directly tied to the dataset's quality, making data curation and quality control critical components in the use of golden datasets.

What is the size of a golden dataset?

The size of a golden dataset for LLM evaluation is contingent on the use case, task complexity, and available resources. Initially, a set of 10-20 examples can suffice to track iterative prompt or model improvements. However, for more intricate use cases or as the product evolves, expanding the dataset to include 100-200 diverse examples is advisable to ensure comprehensive assessment and refinement.

A golden dataset should be meticulously curated. The dataset's quality is paramount, as it directly influences the accuracy and reliability of the evaluation. When surveying public examples, Databricks used a dataset comprising 100 questions from internal documents in one evaluation, while another evaluation utilized 1k QA pairs, each validated three times by two labelers, to test LLMs on long dependency tasks. Talking to Microsoft's Copilot Teams, we found that they recommend 150 question-answer pairs for complex or broad domains.

The dataset must be representative of the LLM's intended tasks, encompassing a wide spectrum of content and contexts to rigorously assess the model's capabilities. A balanced approach to evaluation, combining automated methods (like using another LLM for preliminary assessment) with human evaluation, is essential for a thorough and cost-effective analysis.

Ultimately, the golden dataset should be sufficiently large and varied to encompass all pertinent aspects of the LLM's tasks within your product while remaining resource-efficient for comprehensive evaluation.

How do leading AI teams integrate golden datasets into their workflow?

A golden dataset is a critical component throughout the AI model's lifecycle, serving as a benchmark for initial training, fine-tuning, performance evaluation, and ongoing enhancement post-deployment.

Leading AI teams integrate golden datasets into their product deployment workflow in several ways:

  • Baseline Performance — A golden dataset is used to baseline the performance of each model and succession of models. This is crucial for continuous integration and deployment (CI/CD) tests. The golden dataset serves as a reference point for evaluating the performance of the AI model over time and across different versions.

  • Release Evaluation — Golden datasets play a pivotal role in the release process of new app versions or prompt templates. They are employed to rigorously evaluate AI model performance against critical use-case scenarios and specific data segments. This ensures that any risks associated with the new release are assessed and mitigated, guaranteeing the model's reliability and expected performance in production.

  • New Model Development and Fine-Tuning — Golden datasets are crucial for the development of new AI models and the fine-tuning of existing ones. They provide a reliable benchmark for initial model training and are instrumental in the iterative process of model refinement. By comparing model outputs to high-quality reference data, developers can identify specific areas for performance enhancement and ensure the model's accuracy and reliability before and after deployment.

  • Performance Tracking — As new models are released, AI teams leverage golden datasets to align model performance with organizational OKRs and goals. This strategic approach ensures that each iteration of the model not only meets the technical standards for accuracy and reliability but also contributes to the overarching business objectives. Golden datasets provide a structured framework for measuring progress and achieving continuous improvement in line with these goals throughout the model's lifecycle.

What formats are used for golden datasets?

Golden datasets can be stored in various formats depending on the specific requirements of the organization and the nature of the data. Klu supports CSV and JSONL, but we recommend using JSONL due to its popularity in the OpenAI ecosystem.

Some commonly used formats:

  1. CSV, TSV, or JSONL — These are popular formats for storing structured data, such as tables or lists. They are text-based, making them easy to read and write, and they are widely supported by data analysis tools and programming languages. In the context of Azure Machine Learning, for example, these formats are recommended for test datasets used in batch runs.

  2. Proprietary Formats — Some organizations may use proprietary formats for their golden datasets. For example, BMC uses a specific format for its datasets.

Selecting the appropriate format for a golden dataset is critical and depends on the data's characteristics, the processing and analysis tools in use, and the organization's unique needs. It's essential to choose a storage method that preserves the dataset's quality and integrity while ensuring easy access and usability for stakeholders.

How does golden dataset quality influence evaluation?

The integrity of a golden dataset is paramount in the evaluation of AI models. Such datasets, which are meticulously cleaned, validated, and integrated, serve as the standard for training and testing, ensuring the development of accurate, reliable, and unbiased AI systems. The quality of these datasets directly correlates with the effectiveness and success of AI applications.

High-quality datasets are crucial for several reasons:

  • Model Accuracy — High-quality training data increases the reliability of machine learning models. The accuracy of these models is paramount to their success, and high-quality training data is the only way to increase this reliability. If the training data is not accurately annotated, the model will not be able to provide correct outcomes.

  • Model Generalization — A high-quality dataset accurately represents real-world phenomena, is comprehensive, and is free from biases. This helps the model to generalize well to unseen data and reduces the risk of overfitting, where models perform poorly on new data.

  • Bias Mitigation — Quality training data plays a crucial role in mitigating biases in AI systems. If the data used to train the model is biased, the model's predictions will also be biased, which can have serious ethical implications.

  • Efficiency and Cost-effectiveness — AI/ML models trained on high-quality data require fewer improvements in performance, saving time and money. Companies can quickly deploy the models trained on such data, and less money is spent on retraining.

  • Reliability and Adoption — High-quality training datasets help in creating more efficient and reliable AI/ML models, which can be easily and widely adopted by users for various purposes.

To ensure a dataset's quality, it is crucial to scrutinize the data's origin, ensure it is complete, balanced, and representative. Rigorous data cleaning, accurate labeling, and thorough preprocessing are fundamental to maintaining dataset integrity.


How can synthetic data be used to bootstrap an initial golden dataset for LLM fine-tuning?

Synthetic data, generated by an LLM, can bootstrap a golden dataset for LLM evaluation. This method creates artificial datasets that reflect real-world data structures and patterns for training and assessment purposes.

To bootstrap a synthetic golden dataset for LLM evaluation, follow these steps:

  1. Generate Data: Prompt the LLM to produce synthetic text for the desired domain, ensuring coherence and realism.

  2. Ensure Quality: Validate the synthetic data against real-world examples to ensure accuracy and relevance.

  3. Train the Model: Incorporate the synthetic data into the LLM's training process, using self-training techniques to refine performance.

  4. Evaluate Performance: Test the LLM with the synthetic dataset and compare the results to expected outcomes.

While synthetic data is useful for initial LLM evaluations, it should complement, not replace, expert-validated golden datasets. The initial dataset's quality is crucial — poor-quality inputs will lead to unreliable synthetic data. Diversify evaluation methods by including human assessments for a comprehensive understanding of LLM performance.

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