What are the impacts of data size and its quality on LLMs?

Data on LLMs

In this article, I'm going to explore the impact of data size and quality and how it can affect a large language model. So, we understand that these large language models are really good at text generation and they're great at being just deep learning models, they're transformers, right? At their very core, they're just like any other AI or machine learning model and they just happen to be good at natural language generation. Now these models, since they're able to learn the complexity of human language, their potential is really immense. You can almost tell that these applications are going to be developed for all sorts of things. 

Understanding Data Quality:

Now, even depending on let's say if we're trying to do something like text generation or text image generation or image captioning or all sorts of sectors from finance to telecom and healthcare, they're all going to be using these tools.

Then the question is, is, well what data should I use? What are the things that I can do to worry about data quality since these things aren't just machine learning models or AI at their very core, like any of their deep learning model, input matters. So, garbage in is going to be garbage out. So, you have to make sure that your data quality is at par. So, data quality may refer to a wide range of components and regarding the data at hand. And so, we could have to identify issues that are specific to the task that we're trying to work on. And this could also look at some commonly-encountered issues that are noticed in data quality issues and are standard across most applications and domains and we have to address those. And this is pretty par for the course for any type of AI or machine learning models that we're going to be using.

Data Characteristics and Distribution:

So, the data quality is also referring to the type or the nature of the data. It's not just important that we understand how much of it there is, but we have to understand what format it's in, what it's describing, how varied is it, what is the statistical relevance of this dataset, what does the distribution look like. Because remember, this is a generative AI at its core. It's going to be trying to learn the distribution of our dataset. So, does our distribution in our training data is a pious in any way, those are all things that matter. And so, we have to also make sure that we're using our data for the specific task that we're trying to understand and work with in our large language model. All right, so if this is going to be a medical tool that's going to help medical professionals, well, I have to make sure that my training data consists of medical data, medical text, such that it's able to parse and understand what these tools will do.

Common Data Quality Issues:

Now, there's some commonly encountered data problems that I'd like to do. So overall, the real world data sets are just subject to several issues just in general, such as the missing or incomplete data. That's one thing. This will comprise the learning stages of the models and what you'll also see is that you're going to have imbalanced data which will bias the learning task to more represented concepts. So, if we're trying to neglect or produce erroneous outputs for others, we need to make sure that this biased data is removed. We don't want to have this kind of erroneous data. So, think of it like this. If we're looking at medical data to help a doctor make a diagnosis, well, then the question is what happens if we have someone of a particular race show up in the doctor's office?

How is our data trained? Is it trained only on one race? Are there multitude of races? Are there underrepresented races inside of this dataset that's used in our training? Because that could affect the tool to help this person who's just walked into the doctor's office. So, that all has to matter depending on what we're doing. And so, the data profiling is another tool that helps us. 

Improving Data Quality:

It's important that we automatically assess the data in its characteristics. Are we trying to highlight and handle them appropriately? Are we having tools that provide a comprehensive analysis of the data? This is really important for us to make sure that this is there.

And if we're trying to do our data profiling, ideally what we're trying to make sure is that the process of automatically determining this main characteristics of our dataset is there. And this can come from basic statistics to detailed visualizations. But we're just trying to identify and remedy the data quality issues using these kind of data profiling tools. OK, so here are some steps to improve our data quality. 

Make sure that when we're dealing with our data quality, we have to train our team in the importance of data quality. What is bad data? What is good data? And this is all dependent on the context that you want to use your large language model for. It's important also to educate stakeholders. Now when we're educating our stakeholders, we have to make sure that we're discussing things that the stakeholders are interested in, such as if you're, you know, are you collecting data from a survey?

Well, what questions are you going to have on that survey? How is that going to be represented? What are the terms that you're using? Because all of that could unintentionally bias our large language model. So, this is important that we look at our team, make sure that they understand their key component to making sure our data quality is up to par. And then we have to choose the right data profiling tools is important. There are a few different data profiling tools that are available on the market today, and they all have slightly different features. And as these things become more relevant in society in the next little bit, you're going to notice that there are going to be even more tools that are coming out, and they're all going to have different features and different contexts for using them.

Final words:

Make sure to shop around and understand what if they're going to be useful for your use case when using your large language model. So, using this data profiling tool, educating your stakeholders, and training the team and the importance of data quality will help raise your data quality when you are trying to train your large language model. And these are the main reasons why we should be working on this data profiling techniques and understanding the data quality in our data sets depending on our context. It's very important when it comes to large language model training.

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