Some of it might be stale or expired, while other batches are fresh and delicious.

Now, let’s say we have two different sets of ingredients one that’s been sitting in our pantry for months (low-quality training data) and another that was just delivered today (high-quality training data). When we use the low-quality ingredients to make our LLM, it might not turn out so great. The model might struggle with understanding certain words or phrases because they’re not as common in everyday language.

On the other hand, if we use high-quality training data (like fresh produce from a farmer’s market), our LLM is going to be much better at learning and remembering new information. It will have a wider vocabulary and be able to understand more complex sentences because it has been trained on a diverse range of text.

So, in summary: using high-quality training data can greatly improve the performance of small scale LLMs by providing them with a richer and more varied set of inputs to learn from. This is especially important for models that are designed to be used in specific contexts or domains (like medical research or legal analysis) because they need to have a deep understanding of the language and terminology associated with those fields.

Now, some examples! Let’s say we want to train an LLM to help us write scientific papers for our chemistry class. If we use low-quality training data (like old textbooks or outdated research articles), our model might struggle to understand the complex chemical terminology and equations that are used in modern science.

On the other hand, if we use high-quality training data (like recent journal articles or cutting-edge research papers), our LLM will be much better at understanding these concepts because it has been trained on a wider range of text. This can help us write more accurate and insightful scientific papers that are based on the latest research in our field.

Boom, just like that! The impact of quality training data on small scale LLMs is pretty straightforward: using high-quality inputs will result in better performance and improved accuracy over time. And who doesn’t love a good snack that tastes delicious?