When you’re developing an AI-based application, you need to handle a lot of things. Nothing’s more important than training your large language model. Which means that you have to start with the right LLM datasets.
Think of the dataset like gas for your AI application. Just like with your car, if you put in the wrong fuel, the engine won’t perform. Do you want your app to shine? Then you’ll need to carefully choose the information you feed it.
With that in mind, this article looks at how you can choose the right LLM training dataset. We’ll discuss how you can avoid inefficiency, bias, and errors when you fine tune your model.
Why Dataset Matters in LLM Fine-Tuning
Are we making a mountain out of a molehill? To better understand why we aren’t, let’s look at what happens if bad data creeps in.
Think of AI like a child. Children can be quite gullible, they often accept information you give them without question. Otherwise, we’d never pull off the Santa Claus or Easter Bunny ruses.
AI is even more innocent. It doesn’t know how to sort bad data from good. Now, let’s look at a child again. Let’s say you tell that child that the earth is flat and make them believe you. They’ll go their whole lives arguing that the world is flat unless someone convinces them they’re wrong.
Your AI model is similar. Unlike children, however, it’s not going to be easy to get it to go against its early training. This makes the data you use during this critical phase extremely important.
Types of Datasets Used in LLM Fine-Tuning
Not all datasets for LLM fine tuning are created equal. What works for you depends on the task and how specific you need the data to be.
Task-Specific Datasets
These are tailored to specific jobs like summarizing, classifying, or translating text, like:
- Summarization: XSum, CNN/Daily Mail
- Sentiment analysis: IMDB reviews, Twitter sentiment datasets
Domain-Specific Datasets
Need something industry-specific? These are your go-tos:
- Legal contracts for legal AI
- PubMed or ArXiv for medical research
Custom Datasets
Companies often build their own datasets from internal resources, like support tickets or email logs. This data is often gold because it’s uniquely yours.
Synthetic Datasets
When you’re stuck without enough real-world data, synthetic datasets generated with tools like smaller LLMs or data augmentation can save the day.
Human-Annotated Datasets
Sometimes, only humans can get it right. Manually labeled datasets are invaluable for tasks like classification or named entity recognition.
What Makes a Dataset Effective?
A great set goes beyond relevant. It’s clean, balanced, and legally sound. Here’s what to look for.
Relevance
Choose a set that matches your task. Are you creating a medical chatbot? Train it using clinical data, not Aunt Susie’s natural remedies blog. Back to the analogy for school. We start teaching children a good foundation so they can navigate their world.
Quality Over Quantity
A small, curated dataset trumps a massive, messy one that contains inconsistent or irrelevant data.
You can also look at the method you’re using to train the LLM. Instruction fine tuning can be useful when you’re dealing with smaller datasets. It teaches your LLM to follow a set of instructions rather than using huge datasets.
Appropriate Size
How much data you need depends on your goals. Focused tasks might need just a few thousand examples, while broader projects could call for millions.
You can also choose PEFT fine tuning, where you train the LLM by changing specific parameters. This allows you to reduce the overall amount of data you need.
Diversity
Giving your model varied examples prepares it for anything. Make it more adaptable with different:
- Writing styles
- Dialects
- Scenarios
Are you tackling multiple languages? Include data for each one.
Ethics and Legal Considerations
You have to respect privacy regulations and avoid perpetuating harmful stereotypes. If you don’t you could face expensive penalties.
Balanced Representation
Let’s get back to our child learning in class again. Say you give that child a bunch of algebra problems and only one or two geometry tasks. The child will do much better with the former and battle with the latter.
You need to make sure that you give your model a balanced number of examples so it doesn’t overemphasize the wrong sections.
Cleanliness
Think of finding the right dataset like finding a great recipe. You’ll make sure all your measurements are standardized and that you have the right ingredients. You wouldn’t add garlic to a chocolate cake, for example.
Make sure your data’s as clean as possible.
Build Your Own Dataset
It’s not always possible to find the right fit. That’s okay, because you can create your own training set.
1. Define Your Goals
You can’t hit a target if you don’t know what you’re aiming for. Write out what you want to achieve so your whole team is on the same page. How will you know when you’ve reached your goals?
2. Survey Available Data
Look for datasets that align with your goals. Don’t just grab the first one you see; evaluate its quality and size first.
3. Combine and Refine
Maybe the perfect dataset doesn’t exist. You might have to mix and match information from several sources. Just make sure the final product is consistent.
4. Clean the Data
If the source is full of errors, redundancies, or poorly organized, it’s not going to be much good. It makes sense to clean up the dataset before you give it to your model.
Think of this as you would a movie your children want to watch. You’re going to make sure that it’s age-appropriate before you let them see it. Otherwise, you could find them acting out behaviors that are less than ideal.
5. Test and Adjust
Start with a trial run. How does your model perform running a small section of the dataset? Do you need to fix anything?
Evaluating Your Fine-Tuned Model
No matter how well machine learning goes, you need to check your model. You’ll need:
- A validation dataset
- Real-world examples
- Standard metrics like precision of F1 score
Is your model not quite meeting the mark? Play a little and make adjustments.
Conclusion
It’s not always easy to find the right dataset. But that’s a good thing, because it’s important. The better you feed your model, the better it’ll perform. If you rush this process, you’ll end up with mediocre results at best.
