Fine-Tuning

What is Fine-Tuning?

Fine-tuning is the process of adapting a pre-trained model to perform specific tasks or solve particular problems. This technique is a crucial aspect of modern deep learning, particularly when dealing with large-scale models such as foundation models in generative AI.

The main idea behind fine-tuning is rooted in transfer learning. As we disscussed in previous chapters, rather than training a model from scratch, which requires significant computational resources and massive amounts of labeled data, fine-tuning allows us to use the knowledge a model has already learned. This makes the process more efficient and cost-effective.

Fine-Tuning in a Technical Framework

Fine-tuning in machine learning is done by adjusting the parameters or weights of a pre-trained model. This process requires mathematical foundations in optimization, backpropagation, and gradient descent, which explain how the model's weights are updated. To understand fine-tuning at a deeper, more technical level, let's break down the mathematical principles involved.

What are Parameters and Weights ?

At the core of a neural network is the concept of parameters (also called weights and biases). These parameters are the learned values that allow the model to approximate functions that map inputs to outputs. During pretraining, a model like a large language model learns to minimize a loss function using gradient-based optimization, typically through stochastic gradient descent (SGD) or its more advanced variants (Adam, Adagrad).

The loss function 𝐿 represents how well the model is performing on a given task, and it is computed as the difference between the predicted output 𝑦-hat and the actual target 𝑦. For a classification task, for example, the loss function might be cross-entropy loss (where 𝑦𝑖 is the true label and 𝑦-hat𝑖 is the model's predicted probability for class 𝑖):

The gradient of the loss function with respect to the model parameters is then computed:

Note: The loss function and gradient computation described above are specifically for classification tasks, where the objective is to minimize the difference between the true class labels and the predicted class probabilities. However, for regression tasks, the loss function differ (Mean Squared Error MSE) .

Transfer of Knowledge: Optimization and Backpropagation

As we said earlier Fine-tuning is a specific form of transfer learning. The key here is that the model retains the broad features it learned from pretraining (general language understanding) and then adapts them to the new task. To formally describe fine-tuning mathematically, let's represent the loss function for fine-tuning as:

Where:

• is the loss function (e.g., cross-entropy or mean squared error).
• 𝑁 is the number of task-specific samples in the dataset.

Once this loss function is computed for the task-specific dataset, the optimization process begins. Fine-tuning uses gradient descent or one of its variants to minimize this loss function. The general update rule for parameters in gradient descent is:

where:

• and represent the updated and previous model parameters, respectively.
• is the learning rate, which controls the step size in the parameter space.
• ​ is the gradient of the task-specific loss function with respect to the model parameters.

It's important to note that for fine-tuning, the learning rate 𝜂 is typically much smaller than in pretraining, to avoid drastic updates to the pre-trained weights, which could lead to catastrophic forgetting of general knowledge learned from the large corpus.

Task-Specific Fine-Tuning vs. Domain-Specific Fine-Tuning

Fine-tuning strategies vary depending on the desired outcome, and they can be broadly classified into task-specific and domain-specific fine-tuning.

• Task-Specific Fine-Tuning: Task-specific fine-tuning optimizes the model for a particular task, such as sentiment analysis, question answering, or summarization. The dataset for task-specific fine-tuning is carefully curated to match the problem, ensuring the model learns the nuances required to excel.

• Domain-Specific Fine-Tuning: Domain-specific fine-tuning, on the other hand, aims to specialize the model in a particular field of knowledge, such as finance, medicine, or law. Here, the training data typically consists of domain-specific text corpora, helping the model better understand terminology, context, and stylistic nuances relevant to the domain.

Fine-tuning types

There are various approaches to fine-tuning based on the learning paradigm used. The main ones are supervised fine-tuning and reinforcement learning fine-tuning (RLHF). Each approach has distinct advantages and limitations, depending on the task and available data.

Supervised Fine-Tuning

Supervised fine-tuning is the most common and traditional approach for adapting a pre-trained model to perform a specific task using labeled data. Unlike pretraining, which focuses on learning general patterns and representations from large amounts of unlabeled data, supervised fine-tuning involves refining a pre-trained model on a task-specific dataset.

For example, for a question answering task, the model is presented with input-output pairs, where the input consists of a question and a passage of text, and the output is the correct answer extracted from the passage. The model then fine-tunes its parameters to improve its ability to accurately extract answers from the given context, learning to understand the relationship between the question and the relevant information in the text.

Reinforcement Learning from Human Feedback (RLHF)

RLHF is another fine-tuning strategy that incorporates feedback from humans to guide the model's learning process. This technique is often applied in scenarios where the model needs to generate high-quality outputs based on subjective criteria, such as user preferences or ethical considerations.

In RLHF, the model first undergoes supervised fine-tuning on a task (e.g., answering questions or generating text). Then, it is fine-tuned further by using reinforcement learning. Human feedback is provided as a reward signal, where users assess the quality of the model's output, and the model is trained to maximize its reward through trial and error.

The typical RLHF process involves the following steps:

1. Pre-training: The model is trained on large datasets, using conventional supervised learning techniques.

2. Human Feedback: A set of human evaluators scores the model's outputs.

3. Reward Modeling: The feedback is used to train a reward model, which predicts the reward for a given output.

4. Reinforcement Learning: The model is fine-tuned using reinforcement learning algorithms like Proximal Policy Optimization (PPO) to optimize its performance according to the reward signal.

Fine-Tuning Pipeline for LLMs

The process of fine-tuning a Large Language Model (LLM) can be broken down into several key steps:

1. Defining Objectives Determine the goals of fine-tuning. Are you optimizing for accuracy, fluency, contextual relevance, or another metric? Clearly defined objectives guide the selection of data and evaluation methods.

2. Data Collection and Preprocessing Fine-tuning begins with gathering a high-quality dataset relevant to the task or domain. Preprocessing ensures the data is clean, appropriately tokenized, and formatted for the model. This includes addressing potential biases or imbalances in the data.

3. Model Selection Choose a base pre-trained model that aligns with your needs. For instance, smaller models like BERT might suffice for lightweight tasks, while GPT or T5-based architectures are better for generative or complex tasks.

4. Training Configuration Configure hyperparameters such as learning rate, batch size, and the number of epochs.

5. Fine-Tuning Process Train the model using the chosen strategy. Ensure that regular evaluation is performed during training to prevent overfitting.

6. Evaluation and Validation Evaluate the fine-tuned model against a hold-out validation dataset and assess its performance on task-specific metrics like F1-score, BLEU, or ROUGE.

When to Consider Fine-Tuning Over Alternatives

Fine-tuning is often the preferred approach over alternatives like Retrieval-Augmented Generation (RAG) or prompt engineering when the goal is to refine the model’s core behavior or improve its performance on a specific task. While RAG enhances the model by incorporating external knowledge sources, it does not fundamentally alter the model's underlying understanding, which limits its ability to adapt deeply to the task at hand. Prompt engineering, on the other hand, tailors the model's responses for specific inputs but depends on manually crafting effective prompts. In contrast, fine-tuning modifies the model’s parameters directly, enabling a more robust and lasting adaptation to the task, leading to more significant improvements over time.