can significantly reduce the cost of obtaining data to train large language models. It achieves this by both supplementing real data, as well as addressing its shortcomings. Synthetic datasets have been used to train models since the early 1990s.
Synthetic data is data that’s generated artificially
through simulations, algorithms, or other methods, and is designed to resemble the properties and statistical features of real data. According to Gartner, 60% of all data used in AI applications will be synthetic
by this time next year. In other words, synthetic data will account for the majority of data used for AI development.
The most significant advantage of synthetic data is its abundance and flexibility. Since it's computer-generated, synthetic data can be created in vast quantities and designed to reflect a wide range of scenarios that may be difficult, expensive, or ethically complicated to collect from reality. This makes synthetic data an ideal solution for the shortage of LLM data.
In addition, synthetic data addresses privacy concerns that arise when working with real data that often includes sensitive personal information. Synthetic data, on the other hand, contains no personal details, thereby reducing privacy concerns.
Moreover, synthetic data can also eliminate many of the biases inherent in real-world data, ensuring that LLMs don't discriminate or perpetuate those biases. With these advantages, synthetic data is an indispensable tool for researchers and data scientists in various fields.
When it comes to LLM applications, a significant goal of the model is to generate outputs that sound genuinely human. But, synthetic data poses certain challenges. As we move to specific applications, such as a chatbot that resides on a brand’s website, synthetic data can’t produce outputs that match the brand’s voice as it pulls data from the open web. In the Trust & Safety world, we’ve seen people couch toxicity and threats in highly creative ways, and it is difficult to generate synthetic data that can catch this level of creativity.
Additionally, synthetic data is created with algorithms that pull from the open web. As a result, it does not retain many of the advantages of well-vetted real data, and should never be used on its own to train large language models.
Although generating synthetic data offers many advantages, it can never be a complete replacement for real data, due to its inability to capture the subtle complexities and unforeseen patterns of real-world data, limiting models trained solely on synthetic data.