LLM Tuning Made Simple: Types, Pros, Cons, and When to Use Each

Tuning a Large Language Model — image generated using Adobe Firefly. Prompt: “teaching a artificial intelligent machine on information in documents, websites and databases”

In our first article, I introduced you to LLMs and their applications. Now, let’s explore an important aspect of working with LLMs: Tuning!

In this article, I’ll explain what tuning an LLM is, the different types of tuning, including few-shot learning, their pros and cons, and when to use each type. I will end the article by explaining tuning using a simple example. So, grab your favorite snack, and let’s get started!

What is Tuning?

Tuning is the process of adjusting an LLM to improve its performance on a specific task (such as answering questions) or domain (legal documents). Pre-trained LLMs like GPT-3 and BERT are already powerful but may not perform optimally for specific use cases right out of the box. By fine-tuning the model with additional training, we can enhance its performance and accuracy for our desired application.

Types of Tuning

Like there are many ways to customize your bike, we can tune LLMs in several ways. The five main types in decreasing order of complexity are:

1. Pre-training
2. Fine-tuning
3. In-context learning
4. Few-shot learning
5. Zero-shot learning

Now, let’s learn more about each of these tuning types!

Pre-training

Pre-training is like teaching an LLM the basics of language. It’s like learning how to ride a bike before you start customizing it. The LLM is trained on lots of text from books, websites, and databases during pre-training. This helps the model learn grammar, spelling, general knowledge, and relationships between tables and columns.

Pros:

• Gives the LLM a strong foundation
• Teaches the LLM general language understanding

Cons:

• Takes a lot of time and resources
• Doesn’t focus on specific topics

When to use: Pre-training is the first step in creating an LLM, so you always start with this before moving on to other tuning types.

Fine-tuning

Once your LLM has learned the basics, you can start fine-tuning it. This means training the LLM on a smaller, more specific set of information. For example, if you want your LLM to help answer questions about animals, you might fine-tune it using articles and books about animals.

Pros:

• Helps the LLM do a better job on a specific task
• Doesn’t need as much data or resources as pre-training

Cons:

• Can sometimes focus too much on a small set of information
• It might take some trial and error to get the best results

When to use: Use fine-tuning when you have a specific task or topic you want your LLM to focus on.

Skipping Pre-training and Fine-tuning Steps with Pre-trained Models

Yes, pre-trained models can help you skip the pre-training and even the fine-tuning steps, depending on your use case. These models have already been trained on vast amounts of text data and have acquired general language understanding. Some popular pre-trained models are GPT-3, GPT-4, LLaMA, FLAN UL2, and BLOOM.

Top large langaage models, both proprietary and open source as of March 2023

Using pre-trained models can save you significant time and resources, as the pre-training step is computationally expensive and requires a large amount of data. You can use these models for various tasks, such as text classification, sentiment analysis, question-answering, and more.

However, it’s essential to note that, depending on your specific application, you may still need to fine-tune these pre-trained models on your domain-specific data to achieve the best results. Usually, fine-tuning a pre-trained model requires less data and computational resources than pre-training from scratch.

To get started with pre-trained models, you can explore popular natural language processing libraries like Hugging Face Transformers, which provide easy access to many pre-trained models. Its user-friendly interface helps you filter based on your use case.

Searching for models in Hugging Face based on your use case

In-context learning

In-context learning is a technique in which the LLM uses the context provided within the input to learn and adapt to the specific task. This means the LLM can adjust its behavior based on the examples or instructions you give it, even without fine-tuning.

Pros:

• Can adapt quickly to new tasks
• Doesn’t require fine-tuning for specific tasks

Cons:

• It may not be as accurate as a fine-tuned model
• Depends on the quality and clarity of the input context

When to use: Use in-context learning when you want your LLM to adapt to a task based on the input examples or instructions you provide without fine-tuning.

Few-shot learning

Few-shot learning is a technique that allows the LLM to learn and adapt to a specific task with just a few examples. In this case, the LLM uses the examples provided in the input to understand the task it was asked to do and generate appropriate responses. It lies between zero-shot learning and fine-tuning regarding the model’s reliance on input examples.

Pros:

• Can adapt to new tasks with limited examples
• More accurate than zero-shot learning for specific tasks

Cons:

• Requires well-chosen examples to guide the LLM
• It may not be as accurate as a fine-tuned model

When to use: Use few-shot learning when you want your LLM to adapt to a task based on a small number of input examples without the need for fine-tuning.

Zero-shot learning

Zero-shot learning is when you use your LLM for a task without extra fine-tuning. It’s like riding your bike without any customizations but still hoping it’ll do well. The LLM uses what it learned during pre-training to understand and complete the task.

Pros:

• Doesn’t need any extra training or data
• You can start using the LLM quickly for new tasks

Cons:

• It might not do as well as a fine-tuned model
• The LLM’s understanding might be more general and less specific

When to use: Use zero-shot learning when you don’t have much information about a specific topic or want to start using the LLM quickly without spending time and resources on fine-tuning.

Example

To help you understand the different types of tuning, let’s revisit the diagram we have seen before:

Tuning a large language model

Now, let’s say you want to use an LLM to help answer questions about dinosaurs. Here’s how you could apply the different tuning types:

1. Pre-training: First, the LLM learns the basics of the language and a wide range of knowledge from many different text sources, like books and websites.
2. Fine-tuning: Next, you fine-tune the LLM using a smaller set of information about dinosaurs, like articles and books about these fascinating creatures. This helps the model get better at answering dinosaur-related questions.
3. In-context learning: With in-context learning, you provide the LLM with a few examples of dinosaur-related questions and their answers within the input. The LLM then uses this information to understand the task and answer new dinosaur questions based on your provided context.
4. Few-shot learning: With few-shot learning, you provide the LLM with a few dinosaur-related questions and their answers within the input. The LLM then uses these examples to understand the task and answer new dinosaur questions based on the limited context you provided.
5. Zero-shot learning: Alternatively, you could use the LLM without any tuning on the dinosaur topic. It might still be able to answer some questions based on its general knowledge, but it might not be as good as a fine-tuned model.

Wrapping Up

And that’s it! Now you know all about tuning Large Language Models, including few-shot learning. By understanding the different tuning types, pros and cons, and when to use each, you can make your LLM work even better for your specific needs. I hope you enjoyed this easy-to-understand guide!

Watch for more articles in my series, where I’ll dive deeper into other exciting topics.