Image by author
Large language models have revolutionized the field of natural language processing, offering unprecedented capabilities in tasks such as language translation, sentiment analysis, and text generation.
However, training these models is time-consuming and expensive. That is why tuning has become a crucial step to adapt these advanced algorithms to specific tasks or domains.
Just to make sure we're on the same page, we need to remember two concepts:
- Pre-trained language models
- Fine tuning
So, let's analyze these two concepts.
What is a pre-trained large language model?
LLMs are a specific category of machine learning intended to predict the next word in a sequence based on the context provided by the previous words. These models are based on the Transformers architecture and are trained on a large amount of text data, allowing them to understand and generate human-like text.
The best thing about this new technology is its democratization, since most of these models are under open source license or are accessible through APIs at low cost.
Image by author
What is fine tuning?
Tuning involves using a large language model as a base and further training it with a domain-based dataset to improve its performance on specific tasks.
Let's take as an example a model for detecting sentiments in tweets. Instead of creating a new model from scratch, we could take advantage of GPT-3's natural language capabilities and further train it with a dataset of tweets labeled with their corresponding sentiment.
This would improve this model in our specific task of detecting sentiments in tweets.
This process reduces computational costs, eliminates the need to develop new models from scratch, and makes them more effective for real-world applications tailored to specific needs and objectives.
Image by author
Now that we know the basics, you can learn how to tune your model by following these 7 steps.
Various approaches to adjustment
Tuning can be implemented in different ways, each tailored to specific goals and approaches.
Supervised adjustment
This common method involves training the model on a labeled data set relevant to a specific task, such as text classification or named entity recognition. For example, a model could be trained with sentiment-tagged texts for sentiment analysis tasks.
Learning in few opportunities
In situations where it is not feasible to collect a large set of labeled data, learning rarely comes into play. This method uses only a few examples to give the model a task context, thus avoiding the need for extensive adjustments.
Transfer learning
While all tuning is a form of transfer learning, this specific category is designed to allow a model to tackle a different task than its initial training. It uses the broad knowledge acquired from a general data set and applies it to a more specialized or related task.
Domain Specific Fine Tuning
This approach focuses on preparing the model to understand and generate text for a specific industry or domain. By fitting the model on domain-specific text, you get better context and experience on domain-specific tasks. For example, a model could be trained with medical records to tailor a chatbot specifically for a medical application.
Best Practices for Effective Tuning
To make a successful adjustment, you need to consider some key practices.
Data quality and quantity
The performance of a model during fitting largely depends on the quality of the data set used. Always keep in mind:
Garbage in garbage out.
Therefore, it is essential to use clean, relevant and sufficiently large data sets for training.
Hyperparameter tuning
Tuning is an iterative process that often requires adjustments. Experiment with different learning rates, batch sizes, and training durations to find the optimal settings for your project.
Accurate tuning is essential for efficient learning and adaptation to new data, helping to avoid overfitting.
Periodic evaluation
Continuously monitor model performance throughout the training process using a separate validation data set.
This periodic evaluation helps track the model's performance on the intended task and checks for signs of overfitting. Adjustments should be made based on these evaluations to tune the model's performance effectively.
Navigating the pitfalls in LLM fine-tuning
This process can lead to unsatisfactory results if certain obstacles are not also avoided:
Overfitting
Training the model on a small data set or going through too many epochs can lead to overfitting. This causes the model to perform well with training data but poorly with unseen data and therefore have low accuracy for real-world applications.
Lack of adaptation
It occurs when training is too short or the learning rate is set too low, resulting in a model that does not learn the task effectively. This produces a model that does not know how to achieve our specific goal.
catastrophic forgetfulness
When tuning a model on a specific task, there is a risk that the model will forget the extensive knowledge it originally had. This phenomenon, known as catastrophic forgetting, reduces the effectiveness of the model on various tasks, especially when natural language skills are considered.
data leak
Make sure your training and validation data sets are completely separate to avoid data leakage. Overlapping data sets can falsely inflate performance metrics, giving an inaccurate measure of model effectiveness.
Final thoughts and future steps
Starting the process of fine-tuning large language models presents a great opportunity to improve the current state of models for specific tasks.
By understanding and implementing the detailed concepts, best practices, and necessary precautions, you will be able to successfully customize these robust models to suit specific requirements, thus taking full advantage of their capabilities.
Joseph Ferrer He is an analytical engineer from Barcelona. He graduated in physical engineering and currently works in the field of data science applied to human mobility. He is a part-time content creator focused on data science and technology. Josep writes about all things ai, covering the application of the ongoing explosion in this field.
