The Pros and Cons of Using Synthetic Data when Training GenAI Models

The availability of high-quality training data is critical for building effective Generative AI (GenAI) models. 

Yet obtaining sufficient and diverse real-world data often presents significant challenges -  including privacy concerns, data scarcity, and high costs - that impede 70% of leading organizations from realizing GenAI value.

Synthetic data, which mimics real-world data, offers a promising alternative that addresses many of these issues. Leading tech organizations, such as Google, Meta, and Anthropic, are leveraging synthetic data to overcome common data hurdles and facilitate more robust, efficient GenAI model training.

Despite its potential, the use of synthetic data in GenAI model training is somewhat controversial within the AI community. Critics argue that synthetic data might fail to fully capture the intricacies of real-world scenarios, possibly leading to models that underperform in practical applications. The production of high-quality synthetic data can also be resource-intensive and requires specialized expertise, raising questions regarding its efficacy and cost-effectiveness.  

In this blog, we explore the benefits and limitations of using synthetic data for GenAI model training, bringing further clarity into this ongoing debate to help organizations navigate the complexities of this emerging field.

What is Synthetic Data?

Synthetic data is artificially generated data that replicates real-world events. Instead of being collected through direct measurement or observation, it’s created using algorithms and simulations. 

Synthetic data can be designed to match the statistical properties and patterns of real-world data, making it valuable for different applications - especially in scenarios where real-world data is scarce, sensitive, or expensive.

Synthetic data is created via machine learning techniques, statistical modeling, simulations, data transformation, and programmatic generation. Generative Adversarial Networks (GANs) and other deep learning models, for instance, are frequently used to produce realistic synthetic data. 

Synthetic data offers a secure, economical option for model evaluation, training, and testing that helps improve the accuracy and reliability of AI systems. Generating synthetic data enables organizations to maximize the value and applicability of their original data by expanding and diversifying it. It can also help ensure that data privacy and compliance concerns are addressed by serving as a replica for real data without disclosing sensitive information, like intellectual property.

Pros of Using Synthetic Data for GenAI Model Training

Tech organizations are accelerating their AI programs to seize the numerous business opportunities associated with high-value GenAI use cases, but often face numerous data bottlenecks.

The limited availability of high-quality data online usually leaves AI firms in a position of licensing content from publishers, demanding a significant investment. Additionally, publishers and other data providers are introducing new contract constraints regarding the use of licensed data for GenAI model training. Given the fluid state of the current global AI regulatory environment, these restrictions often result in complex contract renegotiations to redefine data usage. 

Copyright obstacles stemming from scraping websites or reliance on public GenAI tools, like ChatGPT, is another major issue that is increasingly exposing AI firms to lawsuits and non-compliance risks. In other scenarios, GenAI use cases may be too complicated or time-intensive to pursue because the data is hard to acquire, a challenge in healthcare, financial services, and other heavily regulated sectors.

Synthetic data isn’t a new concept in computing fields; this represents a decades-old method for applications like deanonymization of personal data. However, the emergence of GenAI drastically simplifies the path to generating and scaling high-quality synthetic data. 

AI firms are relying on their own AI systems to circumvent these ethical and privacy issues, essentially transforming GenAI tools into synthetic data generation vehicles. 

Basically, an organization uses their own GenAI system to produce content and then feeds this content back into the system to train future iterations of the same system. Synthetic data also enables organizations to improve how AI systems navigate the learning process by incorporating additional explanations for data that the system has trouble processing.

Google DeepMind used this technique to generate over 100 million synthetic data examples in an effort to address Olympiad-caliber geometry problems and advance mathematical reasoning in AI systems. Microsoft’s open source Phi-3 small language model is also a product of synthetic data, enabling the organization to develop a less computationally demanding AI model with robust reasoning capabilities. 

Despite the emergence of successful use cases, the swift adoption of synthetic data in the GenAI era has stimulated some controversy.

Cons of Using Synthetic Data for GenAI Model Training 

Synthetic data poses various concerns regarding its use for AI model training; notably, that abundant quantities of output from GenAI systems will be used as training input for future large language models (LLMs). 

In 2021, Nvidia forecasted that the amount of synthetic data will surpass real data in AI models by 2030. The problem lies in the potential for synthetic data to be polluted with stereotypes that proliferate social and historical biases, escalating existing prejudices against traditionally marginalized populations, languages, and identities. 

In other words - there’s a possibility that synthetic data will perpetuate an infinite cycle of problematic information.

Researchers have demonstrated that data sets sourced online are frequently low quality, intensify harmful stereotypes, and are punctuated by detrimental content, like racial slurs or derogatory speech. GenAI could aggravate this problem by embedding and magnifying these biases instead of objectively portraying the world based on real-world data representations.

Stanford University researchers approximate a 68% surge in synthetic articles released on Reddit and a 131% growth in misinformation news articles published throughout January 1, 2022 and March 31, 2023. Other types of AI generated media have proven equally problematic. 

The viral GenAI music hit, “Heart on My Sleeve,” replicated the voices of Drake and The Weeknd and led to backlash from music publishers and artists due to alleged copyright violations. This example isn’t unique; GenAI-created music is prolific, with companies like Boomy saturating the market with over 14 million tracks - leading Spotify to remove a portion of the AI generated songs from its platform to protect artists.

GenAI outputs also continue to mirror a significant quantity of gender bias and produce corresponding negative content per a recent report by UNESCO International Research Centre on Artificial Intelligence. The report indicates that three critical categories of bias were discovered in GenAI technologies: inaccuracies due to lack of AI system exposure to training data from underrepresented groups; learning bias from the algorithm selection process; and inappropriate associations between certain ethnic populations or genders and psychiatric terms due to deployment biases.

Some researchers have also demonstrated that an AI model developed on ChatGPT output resulted in model collapse, with the system eventually producing meaningless content about jackrabbits when consistently re-trained with synthetic data about British architecture. 

Considering the potential ramifications of GenAI content trained on synthetic data created from troubling online datasets, many industry leaders are proceeding with caution.

How to Responsibly Leverage Synthetic Data for GenAI 

The general consensus within the AI community is that synthetic data can be a valuable alternative to real-world data if it’s approached with appropriate guardrails and human oversight in place. Additionally, organizations should implement strategic GenAI governance frameworks that emphasize human-centric approaches towards the use, deployment, and advancement of this technology. 

Tech organizations can responsibly harness synthetic data for GenAI training by aligning with best practices and ethical guidelines:

• Implement stringent validation methods to ensure synthetic data accurately represents the distributions and properties of real-world data.
• Perform frequent audits and evaluations of synthetic data to maintain robust standards of quality and realism. 
• Maintain detailed records of all synthetic data generation processes, including the algorithms used, what parameters were set, and the rationale underlying these choices.
• Proactively identify and mitigate biases in synthetic data to ensure fairness and inclusivity in AI models. 
• Enforce transparency with stakeholders, such as regulatory entities and customers, regarding the use of synthetic data and the steps taken to establish its feasibility and reliability.
• Ensure that the synthetic data production process doesn’t inadvertently expose or infer personal information from real-world datasets.
• Stay current with evolving regulations and industry standards to ensure synthetic data practices comply with these requirements.
• Safeguard synthetic data from unauthorized access with strong authorization protocols and adhere to secure data storage and management practices.
• Engage with academic institutions and research bodies to promote knowledge sharing and advancement in synthetic data practices.
• Establish feedback loops to continuously enhance synthetic data quality based on model performance and real-world outcomes. 
• Leverage synthetic data in conjunction with real-world data to utilize the benefits of both types of data, which also improves model robustness and performance. 
• Test specific scenarios and edge cases that are rare in real-world data to enhance model generalization. 

Develop a Powerful Marketing Strategy

GenAI remains a nascent technology and its full implications are still unknown. Amid a widening trust gap and evolving AI regulatory landscape, tech organizations must be mindful of the pros and cons of synthetic data for GenAI model training before exploring viable use cases. This approach can situate organizations to derive the numerous benefits of synthetic data while mitigating potential risks.

Leveraging an experienced technology content writing and marketing agency can help technology organizations tailor their AI product and service marketing strategies to target their unique audience. 

Orange Bridge is a multiple-award winning agency offering a diverse range of tech writing and marketing services developed for the world’s leading technology providers. We deploy our copywriting and marketing expertise to help tech organizations foster credibility and industry authority, and facilitate client and user trust. 

Our agency is also experienced at contributing to AI system performance by creating and curating model training data - including creating textual insights and providing error correction and detailed annotation, among other solutions. 

Contact us today to learn how we can help your AI firm succeed with your marketing and messaging strategy.