Synthetic vs. Real-World Data: Which is Better for NLP Models

Gartner has predicted that by 2030, synthetic data will completely overshadow real-world data in language models. With hundreds of AI and NLP applications being developed every day globally, we are already running out of high-quality training data. But does that mean that synthetic data is going to be the future of AI? Does synthetic data actually have the potential to replace human-labeled real-world data to train and develop more advanced and smarter NLP applications? To find answers to these questions, let’s draw a detailed comparison between synthetic and real-world training datasets and see which one can power language models better in the future.

Synthetic Datasets vs Human-Labeled Datasets for NLP Model Training - A Detailed Comparison

Both synthetic training datasets and manually-labeled real-world data have their own limitations and strengths. However, to decide which one can be more effective  for language model training, here’s a detailed comparison of them based on some key aspects:

 

Synthetic Datasets	Human-Labeled Real-World Data
Synthetic data can help in fixing certain biases by artificially creating more diverse data. However, if the machine learning model generating the synthetic data contains inherent biases, those biases can be unintentionally transferred to the synthetic data. This can lead to underrepresenting certain groups, events, or contexts in the generated training data.	Real-world data also carries biases (e.g., cultural, gender, socio-economic). However, when human experts label such data, they can remove these biases by adding more context to the data or validating the information to ensure that training data accurately represents real-world scenarios.
Acquiring synthetic data is cheaper as it can be generated using machine learning algorithms and GAN (Generative Adversarial Networks) models. However, the initial setup is costly as these models need high computational resources for training and fine-tuning. Also, you need to invest in data validation workflows/pipelines to validate the generated data.	Getting human-labeled real-world data is more costly than synthetic data, as you must invest in hiring and training annotators and advanced labeling tools. The cost can further increase if the project is complex and demands subject matter expertise.
It is easy to generate synthetic datasets at a large scale. Once you set up the system, you can quickly generate vast amounts of training datasets synthetically, which is great for cases where real data is hard to find or collect.	Scaling human-labeled data is a little challenging as compared to synthetic data. This is because real-world data is limited, and for large-scale projects, you need a dedicated team of annotators who can handle your growing needs efficiently without compromising the data accuracy/quality.
Since synthetic data is created artificially through machine learning algorithms, it lacks real-world context and depth. Also, its accuracy depends on the model from which it is generated. If the input data of that model is inaccurate or outdated, the synthetic data generated through it will also contain inconsistencies and cannot be trusted.	Real-world data can also consist of inaccuracies or can be outdated with time. However, human oversight and validation can remove those inconsistencies in training data and enhance it with relevant context to maintain its reliability. Utilizing their domain expertise and intelligence, human annotators can label training data for tone, intent, or sarcasm to improve the understanding of language models.
As these datasets don’t need real information, the chances of exposing sensitive or personally identifiable information are less in the case of synthetic data generation. This makes it easier to comply with laws like GDPR or HIPAA.	Privacy concerns are high when using real-world training data as it contains sensitive information. When labeling such data, it becomes critical to anonymize the personal details and maintain strict access control to comply with GDPR, HIPAA, and other regulations.
Synthetic datasets are easier and faster to generate. Hence, the language model deployment time is less with synthetic data generation.	Since data labeling by human experts demands time and effort, the models trained on such data have more time to market.

Real-World Applications: Where One Outperforms the Other

The above table clearly shows that both synthetic data and human-labeled real-world training data have their advantages and disadvantages. In some scenarios, synthetic data generation can have an edge over human-powered data labeling and vice-versa. Let’s see the real-world applications of both data types to better understand which one would work in your favor:

Synthetic Datasets: When They Have the Edge

1. Low-Resource Language NLP Tasks

Synthetic data generation can be ideal in situations where NLP models or chatbots need to be trained for indigenous or less commonly spoken languages that have very limited digital resources or data sources. Since limited data related to such languages is available on the web, a diverse training dataset can only be created through a synthetic data generation approach.

2. Data Privacy Sensitive Domains (Healthcare, Finance, etc.)

Exposing sensitive patient information or financial transaction data for AI model training can be risky and lead to legal penalties due to compliance failure. In such scenarios, synthetic data can be a better choice than real-world data.

 

NLP models for medical chatbots or financial fraud detection can be trained using synthetic patient records or financial data that do not contain actual customer information to comply with GDPR and HIPAA regulations.

3. Covering Edge Cases through Rare Event Simulation

Synthetic data can be used to simulate rare events or address edge cases that help models better recognize unusual patterns, making them more robust in real-world applications. For example, a cybersecurity team can create hundreds of synthetic examples of unusual network intrusion patterns to train AI systems to learn to detect threats that might appear only once in a blue moon.

Manually Labeled Datasets - When They Have the Edge

1. Training NLP Models for Sentiment Analysis

When training a language model or a chatbot to understand human sentiments, tone, and context, human-labeled real-world data works better than synthetic datasets. This is because the data labeled by humans captures the complexities and nuances of human emotions, context, and communication styles, which ML models might miss when generating data synthetically.

For instance, a real-world training dataset annotated by humans can distinguish between "That's just great" as genuine praise or sarcasm based on context, while synthetic data might lack this level of sophistication.

2. Developing Models for High-Stakes Tasks such as Research or Legal Text Summarization

In regulated industries, like Legal or EdTech, relying on synthetic datasets for language model training can be risky (as complete context and accuracy are required). If you need to train NLP models for tasks like legal text analysis, academic research, and case summary creation, you need contextually rich training datasets with no bias or inaccuracies. Hence, manually labeled datasets work better in these scenarios.

Synthetic Datasets Works Best for NLP Models When Combined with Manually-Labeled Real World Data

While synthetic datasets are excellent in terms of scalability, cost-effectiveness, and diversity, they can’t outperform manually labeled real-world data for accuracy and contextual relevance. Hence, it is better to use a hybrid approach - leverage both synthetic datasets and human-labeled data to train NLP models for more complex scenarios. When used together, both data types can complement each other, enabling language models to better understand the complex nuances and perform efficiently in real-world scenarios.

For example:

When building a customer service chatbot that understands both common and uncommon customer inquiries, you can feed both real-world and synthetic data to the language model. Synthetic data will help fill gaps by generating responses for rare queries, while real-world training data will ensure that the chatbot handles the most common and complex queries with high accuracy.

In a hybrid setup, human expertise is critical for two key roles: validating synthetic datasets and refining real-world labeled data. To incorporate human expertise, you can either:

●     Hire data labeling experts in-house and provide them with initial training about your project’s specifications, labeling guidelines, and workflow. This approach works better when you don’t have budget constraints, as significant investment is required for hiring data annotation specialists and providing them with the latest tools and infrastructure.

●     Outsource data labeling services to a third-party provider who has a dedicated team of experienced annotators to handle large-scale projects with precision and efficiency. This approach is more cost-effective as you don’t need to invest in employee training or advanced infrastructure.

End Note

The debate between synthetic and manually labeled datasets isn’t about replacing one with the other—it’s about collaboration. By combining both synthetic datasets and human-labeled real-world data, we can overcome key challenges such as data scarcity, model explainability, and ethical compliance. Together, these datasets can create more capable NLP models that are not only effective but also future-ready.