Scaling smarter: How Knowledge Distillation powers Large Language Models?

In the world of data and AI, large language models (LLMs) like GPT-3, GPT-4, and their competitors have garnered significant attention due to their remarkable capabilities in generating human-like text. These models are powered by billions (or even trillions) of parameters, enabling them to tackle a wide variety of natural language tasks with impressive accuracy. However, there’s a catch: these models are computationally expensive, slow to deploy, and difficult to scale.

This is where knowledge distillation comes in – a technique that allows us to compress large, powerful models into smaller, more efficient versions without sacrificing too much performance. In this article, we will explore the basics of LLMs and knowledge distillation, as well as how they’re being used to make state-of-the-art models more accessible and practical.

What are Large Language Models?

Large language models are a subset of deep learning models designed to understand and generate human language. These models, such as OpenAI’s GPT-3 (with 175 billion parameters) and Google’s BERT (with up to 340 million parameters in large variants), are based on the transformer architecture, which has become the standard for natural language processing (NLP) tasks.

More insights: Large Language Models explained: Learn what they are and how do they differ from GenAI

What is Knowledge Distillation?

Knowledge distillation is a model compression technique that transfers knowledge from a large, cumbersome model (the “teacher”) to a smaller, more efficient model (the “student”). The objective is for the student model to approximate the teacher’s behavior and performance while requiring fewer parameters and less computational power.

The concept was first introduced by Geoffrey Hinton et al. (2015) in their paper “Distilling the Knowledge in a Neural Network.” The key idea is that the teacher model’s output probabilities (or logits) contain rich information that can guide the student model’s learning process. Instead of training the student model on the raw training data, the student is trained to mimic the teacher’s behavior on the same data.

How does Knowledge Distillation work?

Knowledge distillation is a machine learning technique where a smaller, simpler model (student) is trained to replicate the behavior of a larger, more complex model (teacher). The teacher model, which is typically a high-performing neural network, generates predictions or outputs that serve as a learning guide for the student. Instead of learning solely from labeled data, the student model learns by mimicking the teacher’s soft predictions, which provide richer information about the relationships between classes or features.

This approach helps the student model capture the essential knowledge from the teacher while being more lightweight and efficient, making it ideal for deployment in resource-constrained environments or for speeding up inference without significantly compromising accuracy.

Benefits of Knowledge Distillation

The primary benefits of this approach are:

• Smaller model size: The student model is much more lightweight, with fewer parameters.

• Faster inference: The reduced size of the student model enables faster processing, making it more suitable for deployment in resource-constrained environments.

• Lower energy consumption: By reducing the size of the model and the computational complexity, knowledge distillation can help mitigate the environmental impact of training large models.

Knowledge Distillation techniques

While the core principle of KD is relatively simple – transfer knowledge from a large model to a small one – there are various advanced techniques that improve the effectiveness. Below are some of the key approaches:

• Soft Target Distillation: The most common approach to Knowledge Distillation (KD) where the student model is trained to match the teacher model’s soft outputs (i.e., probabilities or logits), rather than the hard classification labels. This extra information helps the student model learn better generalizations. More details in following sections.

• Feature Distillation: Feature distillation involves transferring intermediate representations or activations from the teacher model to the student model. Instead of focusing on the final output layer, the student is encouraged to mimic the activations at various hidden layers of the teacher. This technique can help the student model capture richer representations and more complex features.

• Data-Free Distillation: This approach aims to train a smaller student model without needing the original training data. Instead, techniques like generating synthetic data or using activation functions to simulate the teacher’s behavior allow the student model to learn efficiently. This approach is especially useful when data privacy or availability is a concern.

Teacher-Student framework: The core of Knowledge Distillation

The Teacher-Student Framework is the foundation of Knowledge Distillation, where a larger, more complex model (the teacher) transfers its knowledge to a smaller, simpler model (the student). The Teacher-Student Framework is widely used in applications like mobile AI, edge computing, and real-time systems where efficiency and speed are critical.

Here’s how it works:

Teacher model

• A large, pre-trained model (the “teacher”) serves as the source of knowledge.

• This model is typically accurate but computationally expensive to use in real-world applications.

• It generates predictions, often as probability distributions over classes (called “soft labels”), which provide deeper insights into how it makes decisions.

Student model

• A smaller, lightweight model (the “student”) is designed to learn from the teacher.

• The student mimics the teacher’s outputs, learning both the correct answers and the subtle patterns the teacher has discovered.

Knowledge transfer

• The teacher provides guidance by sharing its soft labels or other features.

• The student is trained using a combination of the original dataset’s true labels and the teacher’s soft labels, enabling it to replicate the teacher’s performance with fewer resources.

Purpose

The goal is to create a smaller model that is almost as accurate as the teacher but much faster and less resource-intensive, making it suitable for deployment in environments with limited computational power.

Read more: Interested in setting up an in-house LLM platform?

Knowledge Distillation in action: Use case explained

As part of my learning, I did a tweet sentiment extraction. The Teacher model utilized was Llama 3.1 405B, which classified the sentiment of tweets, while the student model was trained using Roberta-base. The dataset employed for this task was sourced from HuggingFace. AutoTrain can be utilized to train a student model, which is possible both online and locally. Additionally, it facilitates the uploading of the trained model to the Hugging Face repository.

The following diagram provides a concise overview of the knowledge distillation process:

• As a result of teacher model training, we get the Soft Targets i.e. the predicted probabilities from the (teacher) model providing more nuanced information.

• After initializing the student model, we feed the subset of data to student model and perform sentiment analysis.

• The Forward pass is the process of inputting the data to the (student) model to get predictions.

• Once the forward pass completes, we can compare the distillation loss typically by KL Divergence function.

• In Backward Pass, we essentially compute the gradients of loss that determine how we can adjust the weights to reduce the loss. This is the main step where student model actually learns from the soft targets.

• Next, we re-run the model and compute the performance and keep training student model until we get the desired outcome.

Conclusion

Large language models have transformed the landscape of natural language processing, but their size and computational demands can be limiting factors. Knowledge distillation offers a promising approach to address these challenges by compressing large, resource-hungry models into smaller, more efficient versions without significantly sacrificing performance.

By improving the efficiency, speed, and accessibility of these models, knowledge distillation holds the potential to make advanced AI more sustainable and accessible to a wider range of industries and applications. As research in this area continues to evolve, the combination of LLMs and distillation could well define the future of practical, scalable AI.

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