How are LLM’s Trained ?
Whether to train your own language model or use a pre-trained one depends on your specific use case and resources.
Key Concepts: Learning and Training
Firstly, you need to do both….
- “Learning” refers to the process of figuring out how the input variables are related to the output variable
- “Training” refers to the process of using a labeled dataset to adjust the model so that it can accurately predict the output for new, unseen data.
In other words, learning is about discovering the relationship between the variables, while training is about adjusting the model so that it can make accurate predictions based on that relationship. Both learning and training are important steps in the machine learning process for LLMs.
There are generally two types of LLM training: supervised and unsupervised learning.
Supervised learning involves training an LLM on a labelled dataset, where each data point is associated with a specific target or label. The model learns to predict the target based on the input features. This approach requires a large amount of labelled data, which can be expensive and time-consuming to obtain. However, it has been shown to produce highly accurate models in many cases, especially for tasks such as text classification, sentiment analysis, and machine translation.
The process of labeling data can be done manually, by human annotators, or it can be automated using various techniques such as natural language processing or computer vision algorithms. Once the data is labeled, it can be used to train and evaluate supervised learning models.
Preparing training data for supervised learning involves several steps.
- Data Collection: The first step is to collect relevant data that can be used to train the model. This can involve gathering data from various sources, such as online databases, APIs, or manual data entry.
- Data Cleaning: Once the data is collected, it needs to be cleaned and preprocessed to remove any inconsistencies or errors. This can involve removing duplicates, correcting errors, filling missing values, and transforming the data into a standardized format.
- Feature Engineering: In supervised learning, the input data needs to be transformed into a set of features or attributes that the model can learn from. This involves selecting the most relevant features and transforming them into a format that the model can process.
- Labelling Data: As mentioned earlier, supervised learning requires labeled data, where each input is associated with a corresponding target output value or label. The labeling process can be done manually or automatically, as discussed earlier.
- Manual Labelling: This involves having human annotators assign labels to the input data. For example, in text classification, human annotators might read through a set of documents and assign labels such as “positive” or “negative” to each document. Manual labeling can be time-consuming and costly, but it can produce high-quality labeled data.
The labeled data is typically stored in a file format that can be easily read and processed by machine learning algorithms. There are several file formats commonly used for storing labeled data, including: CSV, JSON and Tensor Flow Records.
Here is an example of how to store labeled data in a CSV file:
age,gender,income,label 22,Male,25000,0 35,Female,45000,1 41,Male,78000,1
In this example, each row represents an example, with the first three columns representing input features (age, gender, and income) and the last column representing the target label (0 or 1).
The specific file format used for labeling data depends on the preferences of the data scientist and the tools and libraries being used for the machine learning project.
- Automatic Labeling: Automatic labeling involves using algorithms to assign labels to the input data. This can be done using various techniques such as natural language processing, computer vision, or clustering. For example, in image classification, an algorithm might automatically assign labels to images based on the presence of certain visual features.
- Semi-Supervised Learning: Semi-supervised learning is a combination of manual and automatic labeling. In this approach, a small portion of the data is manually labeled, and the remaining data is automatically labeled using a machine learning algorithm. This can be a more efficient way to label large datasets, as it reduces the amount of manual labeling required.
Unsupervised learning involves training an LLM on an unlabeled dataset, without any specific target or label. The goal is to learn the underlying structure and patterns in the data. This approach is useful when labelled data is scarce or unavailable. Unsupervised learning has been applied to tasks such as language modelling, where the model learns to predict the next word in a sequence given the previous words.
Techniques for LLM Training
In addition to supervised and unsupervised learning, there are various techniques used to train LLMs, including:
Transfer learning involves training an LLM on a large dataset and then fine-tuning the model on a smaller task-specific dataset. This approach leverages the knowledge learned from the larger dataset to improve performance on the smaller dataset. Transfer learning has been used successfully in many NLP tasks, including sentiment analysis, named entity recognition, and question answering.
Covered in much greater detail here
Curriculum learning involves training an LLM on a sequence of tasks of increasing difficulty. The idea is that the model learns to master simpler tasks before moving on to more complex tasks. Curriculum learning has been shown to improve the performance of LLMs on tasks such as machine translation and text classification.
Multi-task learning involves training an LLM on multiple tasks simultaneously. The model learns to perform multiple tasks at once, which can improve performance on each task individually. Multi-task learning has been used successfully in many NLP tasks, including named entity recognition and semantic role labelling.
Adversarial training involves training an LLM to defend against adversarial attacks. Adversarial attacks involve modifying input data to trick the model into making incorrect predictions. Adversarial training has been shown to improve the robustness of LLMs to these types of attacks.
Reinforcement learning involves training an LLM to maximize a reward signal by interacting with an environment. The model learns to take actions that lead to the highest reward. Reinforcement learning has been used successfully in NLP tasks such as dialogue generation and language generation.
That sounds like a lot of work!
Training your own language model can give you greater control over the training data, as well as the ability to fine-tune the model for your specific needs. However, it can also be time-consuming and resource-intensive, as training a language model requires a significant amount of computing power and data.
On the other hand, using a pre-trained language model can save time and resources, as well as provide a strong foundation for your NLP tasks. Pre-trained language models, such as GPT-3 and BERT, have been trained on large amounts of high-quality data, and can be fine-tuned for specific tasks with smaller amounts of task-specific data. Additionally, pre-trained models often have a range of pre-built functionalities, such as sentence encoding and language translation, that can be readily used.
Ultimately, the decision to train your own language model or use a pre-trained one should be based on your specific needs and resources. If you have ample computing power and high-quality data that is specific to your use case, training your own language model may be the best choice. However, if you have limited resources or need a strong foundation for your NLP tasks, using a pre-trained language model may be the way to go.
In the next post we’ll cover the basics of using a pre-trained model.
How can Dell Technologies help?
Dell is making AI simpler and more accessible.
Over the coming months reference guides and validated solution architectures will be released with guidance on modular and flexible architecture for each use case. Focusing on ease of deployment with pre validated hardware and software stacks (a lot more to come on this)
These solutions don’t just improve data scientist productivity by up to 30% but also deliver 2x performance using our validated guidance
Check Out the Entire Generative AI 101 Blog Series:
- Generative AI 101: Series Introduction
- Generative AI 101 Part 1: Key Concepts
- Generative AI 101 Part 2: How are LLM’s Trained?
- Generative AI 101 Part 3: Pre-Trained Model Fine Tuning and Transfer Learning
- Generative AI 101 Part 4: Inferencing (Running your LLM)
- Generative AI 101 Part 5: Project Helix Dell and NVIDIA Solution Architecture