Large language models are in almost any conversation these days when it comes to artificial intelligence (AI). The main power in these models is the sheer quantity of text data used to train the model. So, it’s no wonder we’re seeing synthetic data being brought into the conversation more and more! Synthetic data helps solve many of the issues around building models with integrity, but also helps solve the very basic problem of quantity needed for these large language models!
What is a Large Language Model?
A large language model (LLM) is an algorithm that can provide a wide variety of natural language processing tasks and are trained using massive datasets – we mean MASSIVE. This allows the model to understand, predict, and generate content. The big need for an LLM is having enough data to train the model.
That’s where synthetic data comes in. Synthetic data involves artificial data sets created by algorithms. These are used to create new data sets or to supplement real data in order to train the AI model, especially when it requires such a massive amount of data!
Top Reasons Why Synthetic Data Can be Helpful to Train a Large Language Model:
Legal Liability: With the rise of AI, concerns over data privacy have risen and the use of synthetic data to train a model can help overcome this concern. Businesses concerned about data privacy and security can still leverage AI, but with the assurance they aren’t leaking sensitive, real-world data.
Fill In Gaps: Data scientists are well aware how gaps in a data set can make for a huge nightmare on a modeling project. Real-world data is often filled with gaps, but synthetic data doesn’t include these gaps. This means your modeling project is trained on more complete data, making your model much easier to build.
Bias Control: One big concern in the growing space of AI is the need to control for bias. While real-world data is full of bias, an AI model trained on synthetic data can be built with bias control and with the knowledge that the synthetic data is representative of all people.
Simplify Data Collection: We all know data collection can be cumbersome and downright impossible to gather. That’s where synthetic data can shine: simplifying the process while even creating data for rare or sensitive situations, such as delicate medical information.
Lower Cost: Another well-known fact with data is how expensive it can be to collect the data you need. Synthetic data can provide the same data needed to build and train a model, but without the extensive process of data collection in the wild wild West of real life.
At first impression, the use of “synthetic” in the name doesn’t sound very promising. However, as you dig deeper into the options, using synthetic data has a ton of benefits when building out a model that simply cannot be ignored. Leveraging synthetic data has proven to be so helpful for security, costs, ease, and more. It’s exciting to see use cases evolve where synthetic data is speeding up the timeline on AI models!
Embracing Synthetic Data for Large Language Model
Large language models are in almost any conversation these days when it comes to artificial intelligence (AI). The main power in these models is the sheer quantity of text data used to train the model. So, it’s no wonder we’re seeing synthetic data being brought into the conversation more and more! Synthetic data helps solve many of the issues around building models with integrity, but also helps solve the very basic problem of quantity needed for these large language models!
What is a Large Language Model?
A large language model (LLM) is an algorithm that can provide a wide variety of natural language processing tasks and are trained using massive datasets – we mean MASSIVE. This allows the model to understand, predict, and generate content. The big need for an LLM is having enough data to train the model.
That’s where synthetic data comes in. Synthetic data involves artificial data sets created by algorithms. These are used to create new data sets or to supplement real data in order to train the AI model, especially when it requires such a massive amount of data!
Top Reasons Why Synthetic Data Can be Helpful to Train a Large Language Model:
Legal Liability: With the rise of AI, concerns over data privacy have risen and the use of synthetic data to train a model can help overcome this concern. Businesses concerned about data privacy and security can still leverage AI, but with the assurance they aren’t leaking sensitive, real-world data.
Fill In Gaps: Data scientists are well aware how gaps in a data set can make for a huge nightmare on a modeling project. Real-world data is often filled with gaps, but synthetic data doesn’t include these gaps. This means your modeling project is trained on more complete data, making your model much easier to build.
Bias Control: One big concern in the growing space of AI is the need to control for bias. While real-world data is full of bias, an AI model trained on synthetic data can be built with bias control and with the knowledge that the synthetic data is representative of all people.
Simplify Data Collection: We all know data collection can be cumbersome and downright impossible to gather. That’s where synthetic data can shine: simplifying the process while even creating data for rare or sensitive situations, such as delicate medical information.
Lower Cost: Another well-known fact with data is how expensive it can be to collect the data you need. Synthetic data can provide the same data needed to build and train a model, but without the extensive process of data collection in the wild wild West of real life.
At first impression, the use of “synthetic” in the name doesn’t sound very promising. However, as you dig deeper into the options, using synthetic data has a ton of benefits when building out a model that simply cannot be ignored. Leveraging synthetic data has proven to be so helpful for security, costs, ease, and more. It’s exciting to see use cases evolve where synthetic data is speeding up the timeline on AI models!