Does Synthetic Data Make LLM Development More Efficient?

Have you wondered how chatbots can give accurate answers to every question you ask?

Or how can AI assistants weirdly complete your sentences?

Or how can AI agents automate and simplify complex tasks?

Or how can AI writing tools draft perfect essays?

The answer lies in LLM development and the data used in pre-training and fine-tuning.

Large language models are the core of these sophisticated AI applications and tools that are helping you streamline, automate, and simplify daily tasks.

However, the actual hero is the data that is used to train these models, which equips them to behave in a specific way. In simple terms, the accuracy with which the model performs a task depends on the data that is used to train it.

But where does all this data come from? Is it reliable and unbiased?

Amid rising concerns about data privacy, a shortage of quality data, and biases found in AI models, scientists are on the lookout for an alternative.

Synthetic data comes as a cost-effective and scalable alternative to real data. In some applications, synthetic data has been found to enhance the performance and accuracy of Large Language models. For instance, a project by Hugging Face using synthetic data for sentiment analysis achieved 94% accuracy compared to the baseline model's 91.6%.

In this blog, we'll delve into how synthetic data is revolutionizing the way we train LLMs, overcoming limitations, and unlocking exciting possibilities in the field of artificial intelligence.

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What Is Synthetic Data?

Synthetic data is artificially generated data that mimics real-world data but does not copy its elements. This type of data is generated through various techniques like machine learning algorithms, stimulations, or GANs that replicate real-world observations or properties but do not include authentic information.

Synthetic data is becoming mainstream due to its capability to reduce biases, include structured variety, and boost the accuracy of the models. Plus, it is found to improve the performance of models where real data is scarce by supplementing the training data with real-world, inspired data.

Most importantly, synthetic data ensures data privacy by excluding the details of real-world individuals. Tech companies around the world are facing legal action due to violations of data privacy and compliance. In such a scenario, synthetic data comes as a boon that copies the properties of real-world data but excludes any authentic information like names, demographics, age, gender, medical records, or other private details.

Let’s understand synthetic data with a simple example.

Assume a hospital wants to develop an AI application to predict patient outcomes and help doctors find hidden medical patterns in patients for better diagnosis. Now the hospital has limited real patient data. Also, they are concerned about patient privacy and do not want to muddle legal issues.

The solution is to generate synthetic data and use it to augment their dataset.

Steps to Generate Synthetic Data:

1.   Data Collection:

Collect real patient data, such as age, gender, blood pressure, heart rate, and diagnosis outcomes.

Ensure the data is anonymized to protect patient privacy.

2.   Model Training:

Train a machine learning model, such as a Generative Adversarial Network (GAN), on real patient data. The GAN learns the underlying patterns and distributions of the real data.

3.   Data Generation:

Use the trained GAN to generate synthetic patient data. The GAN creates new data points that have similar statistical properties to the real data but are not exact replicas.

4.   Validation:

Validate the synthetic data to ensure it accurately reflects the properties of the real data. Check for any biases or inconsistencies.

Example Synthetic Data

Let's say the real data includes patient records like this:

• Patient 1: Age: 45, Gender: Female, Blood Pressure: 120/80, Heart Rate: 75, Outcome: Recovered
• Patient 2: Age: 60, Gender: Male, Blood Pressure: 140/90, Heart Rate: 80, Outcome: Complications

The synthetic data might look like this:

• Synthetic Patient 1: Age: 47, Gender: Female, Blood Pressure: 122/82, Heart Rate: 74, Outcome: Recovered
• Synthetic Patient 2: Age: 58, Gender: Male, Blood Pressure: 138/88, Heart Rate: 82, Outcome: Complications

Applications

• Model Training: The hospital can use the synthetic data to train machine learning models without risking patient privacy.
• Data Augmentation: The synthetic data can be combined with the real data to create a larger, more diverse dataset, improving model performance.
• Scenario Testing: The hospital can test different scenarios and outcomes using synthetic data, helping in decision-making and policy formulation.

In this example, synthetic data enables the hospital to build and test predictive models while preserving patient privacy and ensuring they have sufficient data for robust analysis.

How Synthetic Data Improves The Accuracy & Performance Of Large Language Models?

Here are some notable benefits of synthetic data in large language model development.

Data Augmentation

There are AI applications in industries where the real-world data isn’t sufficient to train the models effectively. Either the data is of very low quality, difficult to collect or costly.

The unavailability of data hampers the training of the LLM and therefore impacts its performance.

This is where synthetic data comes into the picture. It augments the real-world data by providing artificial data with real-world properties to accelerate effective model training.

This helps engineers train models on large datasets, enabling them to execute tasks with more efficiency and accuracy.

One such example is the CodeRL project, where the engineers used synthetic data to boost the performance of the model in code generation.

The project investigates the effectiveness of synthetic code data for training LLMs for code generation. The research team found that incorporating synthetic data led to a reduction in code generation errors compared to models trained only on real code.

Mathematical Reasoning

One of the difficulties that models face is solving mathematical questions or reasoning with accuracy. It might be due to a lack of relevant and quality data during the training.

However, recent studies have shown that the pattern of synthetic questions and answers has significantly improved the accuracy with which the model solves mathematical reasoning.

Projects such as Xwin-Math's expansion of synthetic data and MMIQC's integration of this data with high-quality mathematical pre-training data have proven effective in enhancing model capabilities. These initiatives demonstrate the advantages of strategic data augmentation in mathematical AI applications.

Tool Use & Planning

If you are planning to use AI models in tooling, the scarcity of real-world data is a major roadblock. Add to it the cost of data collection and the risk of data privacy compliance.

Synthetic data proves to be an ideal alternative in this case. Projects like Toolformer and Galactica used synthetic trajectories to train LLMs for API usage. The results showed incredible improvements concerning tool use and selection.

Mitigating Bias

Synthetic data can be designed to eliminate embedded biases present in real datasets.

By generating data that represents diverse populations fairly, AI models can be trained to be more equitable and less prone to discriminatory outcomes. Thus enhancing their overall performance and reliability.

Rapid Iteration and Scalability

The ability to quickly generate large volumes of synthetic data allows for faster model development cycles.

This rapid data generation facilitates experimentation with new AI concepts and helps in continuously updating models to adapt to changing patterns in the data. Thereby maintaining accuracy over time.

Privacy Preservation

Since synthetic data does not contain personally identifiable information (PII), it can be shared and used without infringing on privacy regulations.

This enables organizations to leverage data for model training while complying with legal standards, thus fostering innovation without compromising ethical considerations.

Enhanced Model Training

Synthetic data can be tailored to fill gaps in training datasets, addressing issues such as data drift and scarcity.

By continuously generating fresh data samples, models can be kept aligned with current trends and patterns, which is crucial for maintaining high performance in dynamic environments.

How Does Synthetic Data Contribute To Compliance With Data Regulations Like GDPR?

Synthetic data can contribute to compliance with data regulations like the GDPR in several ways:

Avoiding Personal Data Processing: By using synthetic data instead of real personal data, organizations can bypass the need to obtain consent or rely on other legal bases for processing personal data under the GDPR. This helps avoid the complexities associated with data subject rights, data breach notifications, and cross-border data transfers.

Implementing Privacy by Design: Synthetic data aligns with the GDPR's principle of privacy by design, as it enables organizations to work with data that is devoid of personal information. This helps embed privacy into the data-driven operations of a company.

Mitigating Privacy Risks: Since synthetic data does not contain real personal information, the impact of data breaches is minimized, reducing the likelihood of violating the GDPR's data breach notification requirements.

Enabling Data Sharing: Synthetic data can be used to share data between organizations without the risks associated with transferring real personal data, which is subject to restrictions under the GDPR.

Addressing Data Scarcity: In cases where real-world data is limited, synthetic data can be generated to supplement the training dataset for AI models, without the need to collect and process personal data.

Reducing Bias: Synthetic data can be designed to be more representative and diverse, helping to mitigate biases that may be present in real-world datasets, thereby improving compliance with the GDPR's principles of fairness and non-discrimination.

However, it's important to note that the relationship between synthetic data and GDPR compliance is not straightforward, as the level of anonymity and the specific context of use must be carefully evaluated. 

Synthetic data generated from real personal data may still be considered personal data under the GDPR, and organizations must ensure that the identifiability risk is sufficiently low.

How Does The Cost Of Generating Synthetic Data Compare To Collecting Real Data?

The cost comparison between synthetic data and real data is as follows:

Cost-Effectiveness of Synthetic Data:

The main cost of synthetic data is the upfront investment in building the simulation model, but after that, generating additional synthetic data is exponentially more cost-effective than collecting real data.

Synthetic data can significantly reduce the costs associated with data collection, storage, and management compared to real data.

For large datasets or data with high variability, synthetic data is much more cost-effective to generate than collecting and processing real data.

Costs of Real Data:

Collecting, annotating, and preparing real-world data can be very costly and resource-intensive, especially for large-scale datasets.

Real data also incurs ongoing costs every time a new dataset is required or an existing one needs to be revised.

Tradeoffs:

While the upfront cost of building a synthetic data generation model can be high.

The long-term cost savings from using synthetic data can outweigh this initial investment, especially for applications that require large or frequently updated datasets.

The cost-effectiveness of synthetic data depends on the specific use case and the complexity of the data generation model required.

Conclusion

Overall, synthetic data serves as a powerful tool in the AI development lifecycle, improving model performance and accuracy by providing abundant, diverse, and high-quality training data while addressing the limitations of real-world data.

Its application across various sectors, including healthcare, finance, and IT operations, demonstrates its transformative potential in enhancing AI capabilities.

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