Look Out for These Common Issues When Adapting Large Language Models
Does machine learning confuse you? You’re not alone; it’s a complex task. Large language models (LLMs) seem to offer an out. You can quickly adapt them by feeding in new data.
But you can’t just jump in. You need to understand the common pitfalls when it comes to fine-tuning LLMs. Let’s start by looking at what large language models are. Then, we can look at how to go about sidestepping them.
What is LLM?
A large language model is AI-based and designed to process and create text much like humans do. We train them on huge datasets that allow them to understand:
- Language patterns
- Grammar
- Context
- Meaning
You can fine-tune them for tasks like translation, summarizing, answering questions, or generating content.
The Pitfalls to Avoid When Fine-Tuning Your LLMs
Let’s dive right in.
Bad Source Data
How much data is enough? The answer is usually more than you think. That said, quality counts for more than quantity here. You get out what you put in. Garbage in, garbage out, as the saying goes.
Think of it as if you’re studying to take an important exam. Will you use information you find on a random blog or the textbook your college provides?
You want a source that’s unbiased, accurate, and highly relevant to your chosen domain. More is better here. Where possible, look for diverse datasets in terms of language style, context, and structure.
You should also clean the data thoroughly to remove the following:
- Inconsistencies
- Duplicates
- Irrelevant information
Overfitting
You need to see how your LLM fine tuning performs on the training and unseen data. With overfitting, the model works well during the learning stage but can’t handle real-world data. There’s a higher chance of this when you use a small dataset.
You can counteract this by splitting the dataset into:
- Training
- Validation
- Test datasets
You can also program the model to stop the process if your validation sets degrade. Another good idea is to test your model outside of its primary area. You’ll then see if it’s good at giving general answers.
Not Evaluating Performance in the Real World
Why do most models fail? They do so because people focus more on the training than real-world applications. They might use a very different dataset to what the app will come across online.
What to do? You must make sure that your training data is similar to that in the real world. When you let your app loose, it must be able to deal with even the strangest questions.
Don’t Ignore Bias and Ethical Concerns
Google learned this the hard way. As a result, in February 2024, the company reworked Gemini, their AI image generator. Why did this make headlines?
Gemini was great at creating unique images. The engineers went to great pains to make sure it didn’t adhere to any stereotypes. Unfortunately, Gemini went too far. The model would create images of Vikings as people of color. It also generated a picture of a female pope.
We get what they were trying to do. But it’s a great example of how biases can creep in. In this case, the bias favored women and people of color. That’s not a bad thing per say, but it doesn’t work if you’re looking for historically accurate images or content.
How do you get around this? You can run a biased audit before you begin training. You should also clearly define your ethical guidelines for the model and train it using diverse datasets. You can keep an eye on the results to see if biased behavior crops up.
Not Understanding the Costs Involved
It’s easy to assign AI magical properties. Maybe you see it as a student that you let loose to do its thing. That’s only part of the equation. As with a student, your AI needs resources.
Learners can fuel their studies with caffeine, but machines take up a lot of processing power. There are some shortcuts here. For example, you can preprocess the information.
Forgetting to Evolve Your Model
You’ve finished fine-tuning your model, so you’re all done. Hold off on celebrating for the moment. As time passes, you’ll need to update the model by feeding it new information.
You Rely on Generic Models Too Much
Ready-made models are pretty simple. The companies that design this architecture make it as generic as possible. That way, it’s easier to adjust.
The downside is that you can only adapt them to a certain extent. You can, however, plant your data annotation workflows carefully. In doing so, you’re sure to feed the model the right training information. If you’re unsure of how to do this, you might be better off hiring professionals.
Will this option cost more? It is likely to initially, but you’ll save time and money on failed attempts.
Conclusion
A pre-trained LLM can make it easier for you to develop an AI-based app. But you must research your options before rushing in. Think of it like you would a child. Would you let a toddler watch a violent movie? Of course not, because it’s not appropriate. Not only would it scare the child, but it would also start desensitizing them to violence. They might view that as the way the world works.
An LLM is a lot like a child. It doesn’t understand how to ignore stereotypes and biases. You teach a child how to walk and move. Then, you focus on developing fine motor skills and emotional regulation.
View your LLM as a toddler. Give it the right information to learn from so it can learn the skills it needs. Make sure that the data relates strongly to the domain. This way, your “child” will be able to succeed in the real world.
Sometimes you need a clean slate and should rather start from scratch. It takes longer and it’s more expensive, but you then get the exact results you need. Sometimes you can’t adapt an existing LLM enough.
Not sure what option is right for you? Consider consulting a company that specializes in this field.
