LLM Fine-Tuning: What It Is, Common Techniques, And More

Key Takeaways

• Fine-tuning refers to further specializing large language models on small datasets to improve the benefits of performing specific tasks.
• Transfer learning and fine-tuning are not the same.
• Fine-tuning can improve ROI from AI by making it better at performing company-specific tasks and providing more efficient automation.
• Businesses should turn to fine-tuning whenever they face challenges, would like to stay competitive in the industry, or have opportunities to grow.

What Is LLM Fine-Tuning?

LLM (large language model) fine-tuning refers to customizing general LLMs to improve their knowledge in a specific domain and performance on specific tasks (uch as data extraction) or more complex workflows (like insurance underwriting).

An LLM can also be fine-tuned for specific industries, especially those that are highly regulated and require high accuracy – such as healthcare, insurance, and banking.

Fine-tuning is done using smaller datasets specific to a desired use case.

The best way to think about it is as a specialization process that tailors a general LLM to specific needs, enhancing the benefits, efficiency, and overall usefulness.

Pre-Trained Models vs. Fine-Tuned Models

Generalistic pre-trained LLMs are incredibly versatile and advanced and can perform many different tasks from many different domains. However, they aren’t trained to excel at specific use cases nor to acquire specialized knowledge in any specific field.

Fine-tuned LLMs, on the other hand, can perform a wide variety of tasks (thanks to the generalistic knowledge gained during pre-training), but perform better on the specific tasks they’re trained on.

What Is the Purpose of Fine-Tuning?

The main purpose of fine-tuning is to help a model trained for a general context perform better on specific tasks – mainly those that will lead to bigger benefits for companies.

Fine-tuning an LLM helps improve accuracy, efficiency, and the ability to perform very specific tasks by training the model on task-specific datasets. This helps it gain a deeper understanding of specific jargon, styles, and knowledge.

In general, the purpose of fine-tuning is all about leveraging the power of AI models for unique needs. Fine-tuning is a process that transforms a broad tool into a very precise tool for targeted tasks.

Example of Tuned LLM

A general LLM is a good starting point to help provide faster responses and automate customer support, or at least help support agents during the busiest hours. The problem with a general LLM, however, is that it can’t provide much help with the company’s services, products, or company-specific queries.

To provide more personalized support, a company can fine-tune the LLM on a smaller but much more specific data – such as:

• Its past customer service conversations (including chat logs and emails)
• FAQs (e.g. to provide more context on products and terms of service)
• Brand guidelines

After tuning an LLM using such data, a chatbot will become more efficient in handling company-specific queries and automating more tasks without needing human intervention.

As a result, customers will get help faster, customer satisfaction will increase, and human customer support agents will get help handling a high volume of queries.

Chatbots can also be trained to update customers on new or relevant products and services based on purchase history. It’s just one of many ways companies can upsell and increase revenue without having to increase the number of employees.

Factors to Consider When Fine-Tuning

Fine-tuned LLMs include several factors that should be considered to ensure precise results, such as better efficiency, convenient practicality, and easier deployment.

With so many points to consider, the key factors include:

• Data quality and quantity
• Task specificity
• LLM – model size and type
• Computational resources
• Evaluation metrics
• Ethical considerations

Ensuring accurately labeled and representative data is critical. Higher-quality data yields better results, and a large dataset on a specific area is always better than a small dataset. However, there’s a fine line between a lot of quality data and a lot of irrelevant data.

It’s best to use LLMs that already perform well on desired tasks. Since such LLMs are made for similar or the same types of tasks, it’s much easier to fine-tune them to be much more effective for the company’s needs. The more complex tasks are, the more fine-tuning needs to be done.

💡 The more different the target task is from the model’s original task, the more (usually technical) work it will require. For example, it may require completely replacing the output layer in a neural network.

Using larger LLMs makes more sense since they include more parameters, but fine-tuning them will also require much more resources.

When fine-tuning an LLM, it’s important to define goals and improvement marks you’d like to achieve. The most common goals include:

• better accuracy,
• improved user satisfaction, and
• better fluency.

These evaluation metrics will help you fine-tune an existing LLM and gradually improve the LLM’s performance while being able to track the results.

Lastly, it’s important to pay attention to the dataset and model outputs to identify potential biases. This will ensure that the tuned LLM is ethically safe to use.

We specialize in fine-tuning but also customizing existing LLMs, so companies don’t have to start a new model from scratch and can gain way more efficient results with a faster deployment at a reduced cost.

4 Most Common Fine-Tuning Methods

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The 4 most commonly used fine-tuning methods include:

• Zero-shot learning
• Few-shot learning
• Transfer learning
• Hyperparameter tuning

Zero-Shot Learning

Zero-shot learning is a method where an LLM with pre-existing general knowledge is trained to perform tasks it hasn’t been previously trained on. It relies on the model’s pre-existing knowledge (and language understanding) to perform previously unseen tasks.

This method is often used for very specific use cases for which there’s little to no training data available. What’s most unique about this fine-tuning method is that it can train a model and help it adapt to new challenges without using explicit training data,

Few-Shot Learning

Few-shot learning relies on using a small dataset (“a few examples”) to train a model to perform specific tasks.

This method helps models learn and perform new tasks when the data for training is limited. It’s a great way to teach a model to perform tasks it couldn’t perform before in the shortest amount of time possible.

What’s most unique about few-shot learning is that it allows for model adaptation using limited data.

Transfer Learning

Transfer learning refers to “teaching” an existing LLM trained to perform a specific task to handle a similar or related task. This method leverages a model’s existing knowledge to accelerate the learning process with minimal data.

Some consider fine-tuning completely different from transfer learning because it’s more comprehensive:

• ‍Fine-tuning – or full fine-tuning – refers to adjusting the entire model, i.e., the weights of every layer of the model.
• ‍Transfer learning, on the other hand, usually involves freezing the majority of the model’s layers to make only minimal changes necessary for improving the model’s performance on a specific use case.

Full fine-tuning usually results in better performance on target tasks, but transfer learning is quicker and often positively impacts model versatility.

Hyperparameter Tuning

Hyperparameter tuning helps optimize the model for specific tasks by introducing optimal hyperparameters.

Hyperparameters are parameters that are set before the fine-tuning process. They control features such as the learning rate, model architecture, and regularization – all of which impact model performance.

There is a significant difference between hyperparameters and model parameters. Model parameters are the parameters the model learns during the training process, while hyperparameters are, as we said, always manually set before the training process.

This method has a good track record of generating desired results. However, it usually takes a lot of technical expertise and is time-consuming.

Additional (Task-Specific) Ways to Fine-Tune a LLM

1. Task-Specific Fine-Tuning

Task-specific fine-tuning involves making adjustments highly focused on one target task, with an additional focus on improving performance for this task alone. It aligns the model's functionalities tightly with the stipulations of a specific task, thereby optimizing its response and performance metrics.

The layers, learning rate, and other parameters are carefully adjusted to maximize performance on the desired task, utilizing task-specific training examples and data.

💡 Businesses aiming for a niche solution can employ task-specific fine-tuning to create products or services that excel in delivering desired results. This can result in achieving product differentiation in the marketplace.

2. Multi-Task Learning

Multi-task learning focuses on improving a model’s performance across various related tasks. This technique relies on the fundamental belief that simultaneously optimizing a model for a variety of tasks allows it to learn a richer set of features. This results in a well-rounded model ideal for handling a diversified set of tasks.

In this technique, the model is trained to share representations across different tasks. That way, the features and patterns learned during fine-tuning for one task help boost performance on others.

💡 For businesses offering multifaceted services or products, multi-task learning can be a strategic choice, taking advantage of an AI solution that delivers across various areas of business.

3. Sequential Fine-Tuning

Sequential fine-tuning involves a staged process where the model is successively tuned for different tasks, building on the optimizations achieved in each previous step.

It nurtures a cumulative knowledge build-up, ensuring the model gains a rich repository of learning derived from a range of tasks. This promotes understanding and ability to perform across a variety of complex functionalities over time.

This technique integrates a sequence of fine-tuning processes, where each stage builds upon the knowledge and adjustments acquired in the preceding phase, facilitating a continuous enhancement in the model's ability to handle complex, evolving tasks.

💡 For businesses involved in research and development, sequential fine-tuning can be instrumental, fostering a progressive enhancement in solutions while aligning with evolving market demands or regulatory standards.

4. Behavioral Fine-Tuning

Behavioral fine-tuning steers the fine-tuning process towards modulating the model's behavior in line with specific requirements or guidelines.

It often entails integrating specific behavioral traits, ethical guidelines, or communication styles into the model, molding its operational dynamics to resonate with predefined behavioral benchmarks. This ensures the AI system operates within a designated framework, ensuring consistency and adherence to, for example, a company’s guidelines.

This technique involves training the model on examples of the desired behavior to ensure that the output aligns with the predefined behavioral parameters.

💡 This technique can be critical for businesses aiming to adhere to stringent regulatory guidelines or to craft products with a distinct behavioral imprint.

By carefully selecting and implementing the right fine-tuning technique, businesses can strategically steer their LLMs to align perfectly with their objectives, creating models that are not only robust and efficient but also resonate well with their brand identity and operational dynamics.

LLM Fine-Tuning: Key Components

1. Model Architecture

Model architecture refers to the design and structure of an LLM, which also includes the arrangement of the components, connections, and layers. All these factors impact the model’s capacity, performance, and efficiency, among other things.

In general, the LLM architecture is characterized by attention mechanisms, encoder-decoder structure, and efficient parallelization capabilities. These are all important in enabling advancements in language modeling, text generation, machine translation, and other capabilities.

The transformer architecture is most prevalent and has revolutionized natural language processing. It’s best known for its self-attention mechanisms which allow it to capture dependencies between words in input sequences – i.e., to effectively understand relationships between words within input.

2. Target Dataset

The target dataset is a collection of data used to adapt the model and improve its performance on a particular task or understanding of a certain domain.

It can include a wide range of language styles, scenarios, and possibilities if the goal is to enable a very efficient and versatile output. At other times, a more narrow dataset may be more appropriate. This largely depends on the results we’re after.

However, data “organization” is just as important as having quality data. Some of the steps to consider here include:

• Structuring your datasets logically (e.g., by category or intent).
• Performing data cleaning to remove duplicates and irrelevant entries.
• Accurately labeling your data in the case of supervised learning.

A well-organized and high-quality dataset has a higher chance of yielding precise results. Additionally, ongoing collection of data will help an LLM refine and provide even better results in the long run.

3. Training Parameters

Training parameters are set before the tuning process and serve as elements that guide it. They help the LLM learn better.

Learning rate is one of the most important parameters because it determines how quickly or slowly a neural network updates its weights during training. This can significantly affect the model's convergence and overall performance.

Another important training parameter is the batch size. Batch size helps increase or reduce the learning gradient whereas the higher number of batches can help reduce noise and learning inconsistency in data.

4. Outcome

Hopefully, the outcome of the fine-tuning process is an LLM that performs better on specialized tasks, is more accurate, and understands domain-specific context better.

For example, the model may gain a deeper understanding of features such as industry jargon, nuanced language, and even complex concepts in specific industries, like healthcare. As a result, such tuned LLMs can provide much more specific answers, predictions, classifications, and more.

Within companies, this can further lead to much better operational efficiency, higher-level automation, and even improved customer satisfaction.

LLM Fine-Tuning: Step-by-Step

Here are the main things to consider when fine-tuning a model:

1. Defining goals
2. Gathering data
3. Preparing the training dataset
4. Choosing LLM model to fine-tune
5. Fine-tuning process
6. Evaluating the tuned model
7. Adjusting and improving

Defining Goals

The most important thing to do before fine-tuning an LLM is to define goals. Goals can be anything from understanding industry jargon, writing specific types of content, or answering specific questions with minimal human intervention.

Setting goals is crucial for assessing model performance and deciding whether it needs further adjustments after initial fine-tuning.

Gathering Data

Another crucial step is finding data that’s specific and related to target tasks. Ideally, you’d collect examples of desired input and output data. The quality of the gathered data will very much impact model performance, so give it some extra attention.

Preparing the Training Dataset

Once you’ve gathered your data, it’s time to prepare it for training – which means formatting it in a way that helps the LLM learn. For example, you could create input-output pairs, clean and preprocess the text, tokenize the sentences, and normalize the data.

Text Completion:- Input: "The quick brown fox jumps over the lazy"
- Output: "dog."

Machine Translation: - Input (English): "Hello, how are you?"
- Output (Spanish): "Hola, ¿cómo estás?"

Examples of input-output pairs.

Choosing The Base Model

When choosing a base model, consider:

• The data the model is trained on
• The model architecture and size (bigger isn’t always better – consider the computational resources you have available)
• Current performance on target or similar tasks (e.g. if your goal is to train the model to classify images, test how well it currently performs on more general classification tasks)
• Whether you can legally use the base model for your intended purpose, especially if your project is commercial

Fine-Tuning Process

When you’ve set your goals, collected and prepared your data, and selected an LLM, you can begin fine-tuning it using your chosen technique.

Evaluating the Fine-Tuned Model

Before deploying your model, you should always evaluate its performance – preferably on a specifically designed test set. Some of the metrics used to evaluate the finished model include accuracy, relevance, and other task-specific metrics.

Evaluation helps to understand how well the model utilizes the new data and how well it performs in the real world with the new knowledge for specific tasks.

Adjusting and Improving

Depending on the conclusions from the evaluation session, if adjustments are needed, that’s the right time to make them. Adjusting is a process of returning to the second step where more data is gathered, hyperparameters are tweaked, and the model is tuned further.

Difference Between Fine-Tuned and Pre-Trained LLMs

The main difference between tuned and pre-trained LLMs lies in the amount and type of data they’re trained on.

Pre-trained models are trained on massive amounts of data from all types of sources, including books, articles, websites, and others. This gives pre-trained models a vast knowledge of almost any topic. What’s often missing, however, is specialized knowledge of tuned models.

Fine-tuned models are trained on specific and carefully gathered training data (mostly company data) with a specific goal in mind (performing a specific task). Such models are often trained using less data – but data that is much more relevant to the specific target task.

When it comes down to performance, tuned LLMs usually outperform pre-trained models because they’re specifically tailored for a certain task. Pre-trained LLMs make a great starting point for the fine-tuning process.

What Are The Benefits of Fine-Tuning?

The main benefits of a tuned LLM include:

• Optimized performance for specific tasks
• Data and resource efficiency
• Relatively fast deployment (vs. building a model from scratch)
• Flexibility
• Transfer learning ability
• Continuous learning opportunity

In most cases, the tuning process teaches the model to handle specific and complex challenges in specific industries. These tuned LLMs are ideal for highly regulated industries like healthcare, insurance, and banking.

They come equipped with deep knowledge that can be relevant to both a particular company or an industry. For example, an LLM can be trained on information about a company’s products, services, policies, and more.

Leveraging company data to optimize model performance also helps boost the efficiency of company resources. Feeding model-specific, quality, and well-organized data is more cost-effective than building a model from scratch – and can enable automation that requires much less human intervention.

This allows companies to improve the efficiency of their workflows, reduce time spent on time-consuming tasks, and improve the quality of their service.

Another benefit is that a fine-tuned model can easily be adapted to perform other, similar tasks. Lastly, the model can always evolve and adapt if we continue fine-tuning it.

When Should Businesses Fine-Tune LLMs?

Businesses often look into fine-tuning LLMs when they face challenges or aren’t gaining expected benefits and results from off-the-shelf models.

Fine-tuning leads to customized solutions that can further improve workflow, increase productivity and efficiency, and better meet company-specific needs.

Additionally, fine-tuning an existing LLM can give companies a competitive edge and help increase their revenue. For example, they can leverage fine-tuned LLMs to get data-driven insights that help them make informed business decisions, understand the industry’s needs, and provide what the customers need.

Fine-tuning LLMs can lead to better strategic goals, better data availability, and multiple outcome improvements, such as enhanced service/product or internal processes. These are some of the reasons why a business might want to look into fine-tuning an LLM.

Interested In Fine-Tuned LLMs For Your Business? Let’s Talk.

Whether it is to unlock new potentials, increase revenue, innovate better, or gain a competitive edge, fine-tuning an LLM is a game-changer.

If you’re looking to overcome your unique challenges, use your data better, and reach your strategic objectives, please schedule a 30-minute call with our experts to learn how we can help you leverage fine-tuned LLMs for your business.

FAQs

How hard is it to fine-tune an LLM?

Fine-tuning an LLM is a time-consuming and tricky, but very rewarding process.

How long does fine-tuning an LLM take?

Fine-tuning an LLM can take anywhere from 3 hours to a few days or even weeks. The time it takes depends on the amount of data, the number of parameters, and the complexity of the task the LLM is tuned for. Additionally, it depends on the base model being used.

How much data do I need to fine-tune an LLM?

A recommended number of data to fine-tune an LLM is at least 1,000 examples (input data and desired output data) per task.

Is fine-tuning better than transfer learning?

Transfer learning is usually a better choice when you have limited training data available and are using a model that already performs well on tasks that are similar to your task. It is also a better option for those without extensive technical expertise.

Fine-tuning, on the other hand, is usually better when using a base model that needs more training to perform well on your desired tasks and yield the precise results desired.

How much does it cost to fine-tune an LLM?

The cost of fine-tuning an LLM depends on the model size and complexity, but it can range anywhere from a few hundred to a few thousand dollars.