That was very much evident even from back ehwn the first GPT's came out. The moment you started introducing synthetic data, the quality plummeted.
But there is another use case where LLM's can truly help with synthetic data: the more classical classification and regression problems - specifically gathering training data. I had this exact case at work two days ago: A large dataset with a small subset of labeled data. For a binary classifier, there was a huge imbalance in the data - the ratio was roughly 75-25%. I did not have the desire to do all this manually so I used an LLM to get a list that would even out the numbers(and get a 50-50 ratio). And using the data I had, plus the additional synthetic data, the accuracy of my small classifier ended up picture-perfect(given that my actual target was "85-90%" accuracy and the actual result was just shy of 99%).
But there is another use case where LLM's can truly help with synthetic data: the more classical classification and regression problems - specifically gathering training data. I had this exact case at work two days ago: A large dataset with a small subset of labeled data. For a binary classifier, there was a huge imbalance in the data - the ratio was roughly 75-25%. I did not have the desire to do all this manually so I used an LLM to get a list that would even out the numbers(and get a 50-50 ratio). And using the data I had, plus the additional synthetic data, the accuracy of my small classifier ended up picture-perfect(given that my actual target was "85-90%" accuracy and the actual result was just shy of 99%).