How Synthetic Data Can Be Used for Large Language Models

Large language models are at the forefront of the minds of many when they think of any type of artificial intelligence. What makes them tick is that these models are trained on massive amounts of text data. Often the sources of much of this data are what is publicly available online through web scraping.

The reality is that the data, more precisely the sheer amount of data required to train an LLM property, is massive. This means that the acts of collecting and labeling these quantities of data can be expensive. And this doesn’t even touch on the sensitive nature of some data. That’s because some data may be sensitive or confidential, and it may not be possible to share it publicly.

This is where synthetic data comes in. Synthetic data is artificial data that is created by algorithms. It can be used to supplement real-world data or to create new data sets altogether. These data sets are able to train LLMS and even help them be deployable with fewer legal issues and costs. But that’s just two reasons.

So let’s take a look at a few reasons why companies are looking to synthetic data to train their large language models.

Liability and legal issues

It was touched on briefly above so let’s expand. If you’ve been paying attention to the news surrounding LLMs, there has been a growing concern about the use of data garnered through webscrapy. That’s because often lots of private data can be caught up and depending on local laws, there could be issues.

Synthetic data, on the other hand, does not contain any personally identifiable information or what’s called PII. So as it stands there are no liability or legal issues associated with its use in training models. This is important for businesses that are concerned about data privacy, security, and future liability as governments are quicking building legal frameworks to govern AI and personal data.

No anomalies

I’m sure this is a big one, but with synthetic data, you are likely to end up with data free of anomalies and errors as data sets tend to be complete and labeled accordingly. As you can imagine, this can help to improve the performance of LLMs, as they will not be trained on data that is inaccurate or misleading.

Filling in gaps

Synthetic data can be used to fill in gaps in real-world data sets. As many data scientists know all too well, oftentimes times datasets can be missing plenty of important information. These gaps can wreak havoc on any modeling project, but with synthetic data, these gaps aren’t present and you’re likely not to train your LLM on data that’s either incomplete or unavailable.

Control for bias

Synthetic data can be created to control for bias. This is important for ensuring that LLMs are not biased against certain groups of people. The thing is bias can be introduced into data in a number of ways, such as through the way data is collected, the way data is labeled, or the way data is used to train an LLM.

However, by using synthetic data, one can control for bias by ensuring that the data set is representative of all groups of people.

Collects difficult data

And at the end of the day, getting data can become quite difficult to collect. So this is another point that helps synthetic data shine. Teams have to expend fewer resources on capital and man hours collecting vast amounts of data to begin training their LLM. And to be honest, a lot of data may be difficult or impossible to collect in the real world. Teams that use synthetic data are in greater control of the data they use so they can even go so far as to create data about rare events or data that is sensitive or confidential, such as with delicate medical information or time-series data.

Other reasons

There are a few other reasons why teams are considering using synthetic data. From improving overall performance, the reduction of costs, greater data security, and of course the ability to become more flexible. Synthetic data has many reasons why it’s become the tool of choice for training LLMs.

Conclusion

As you can see, synthetic data is a versatile tool that many within the AI world are looking for in order to train their models. But there’s a lot more that wasn’t covered today if you want to get a proper understanding of both synthetic data and large language models. To cross that bridge, you’ll want to come and join us at ODSC West.

With a full track devoted to NLP and LLMs, you’ll enjoy talks, sessions, events, and more that squarely focus on this fast-paced field.

Confirmed sessions include:

• Personalizing LLMs with a Feature Store
• Understanding the Landscape of Large Models
• Building LLM-powered Knowledge Workers over Your Data with LlamaIndex
• General and Efficient Self-supervised Learning with data2vec
• Towards Explainable and Language-Agnostic LLMs
• Fine-tuning LLMs on Slack Messages
• Beyond Demos and Prototypes: How to Build Production-Ready Applications Using Open-Source LLMs
• Automating Business Processes Using LangChain
• Connecting Large Language Models — Common pitfalls & challenges

What are you waiting for? Get your pass today!

Originally posted on OpenDataScience.com

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