Understanding Fine Tuning in Deep Learning

Deep learning is a subset of machine learning (ML), which offers valuable insights and automation capabilities across various industries. However, adapting these advanced models to meet specific business needs isn’t straightforward.

One way to handle this problem is to use a technique called fine-tuning. It boosts the model’s performance in specialized tasks for certain business needs.

What is Fine-Tuning?

Fine-tuning is a technique used in deep learning to enhance pre-trained models and improve their performance at specific tasks. Fine-tuning consists of leveraging a model trained on a larger dataset called a pre-trained model and making precise adjustments during the training process to tailor it toward specific tasks.

In particular, fine-tuning is helpful for businesses that need high precision in specialized domains like healthcare or insurance but don’t have vast amounts of training data needed to train a model from scratch.

What is the Point of Fine-Tuning?

Fine-tuning in deep learning is a technique that goes beyond enhancing model performance - it helps align business goals with more advanced AI models. Moreover, a fine-tuned model acts as a bridge between generic pre-trained models and tailored solutions that meet specific business needs.

The fine-tuning process lets businesses seamlessly integrate AI models into their existing workflows. By adjusting the deep learning model to understand specific data contexts, businesses can get more relevant AI outputs that improve efficiency and effectiveness.

Another reason fine-tuning is important is that adapting technological solutions is key to dealing with rapidly changing market conditions. It lets businesses quickly deploy AI technologies by adjusting well-understood and tested models to new purposes, saving time and reducing the risk of developing an entire model from scratch.

Fine-tuning specific datasets also lets businesses manage risks more effectively. This is especially useful in industries like healthcare and finance, where predictive accuracy can make a big difference in decision-making processes and outcomes.

Fine-Tuning Benefits

Aside from boosting model performance, fine-tuning provides a massive range of business benefits that can improve your business’ operational efficiency.

Cost Reduction

Developing a model from scratch is costly and resource-intensive. Fine-tuning uses existing models to reduce development costs drastically. It lets businesses get the most out of an advanced AI model with a smaller investment, making it a cost-effective solution that lowers the entry barrier for smaller organizations.

Better Customer Experience

Fine-tuned models provide more accurate and personalized responses to customer inquiries, improving customer satisfaction. One example is in insurance, where models that accurately assess risk can lead to more personalized insurance packages.

Competitive Advantage

Businesses can differentiate themselves in crowded markets by deploying models tailored to specific tasks and capable of meeting customer expectations. Fine-tuned models can provide unique insights and automate processes in ways that competitors won’t be able to replicate easily, creating a competitive advantage.

Optimal Use of Resources

Improving the efficiency of AI models helps businesses make the most of their computational resources. Models that are more accurate require less manual intervention and can process tasks faster, reducing the load on IT infrastructure and lowering operational costs associated with data processing and storage.

LLM Fine-Tuning: Examples

Fine-tuned LLMs have interesting applications inindustries like insurance, healthcare, and finance.

1: DMC GPT3.5 for Mortgage Application Workflow Automation

Direct Mortgage Corp employed GPT3.5 to automate their mortgage application process. By fine-tuning GPT3.5, along with LLaMa-2 and LightGBM, they were able to use tailored AI Agents for document classification and data extraction.

This application significantly speeds up the approval process in the following ways:

• Handling over 200 types of documents
• 20x faster time-to-approval
• 80% cost reduction per processed document

It shows the impact of domain-specific fine-tuned LLMs in financial services - especially when it comes to complex, document-heavy workflows.

2: Med-Palm2 for Answering Medical Questions

Developed by Google Research, Med-Palm2 has exceptional capabilities when providing high-quality answers to medical questions, leading to improved medical training and patient care. It achieved top results on medical exams, such as USMLE, surpassing passing scores by understanding complex medical data and customer health inquiries.

This makes it a highly useful tool for generating precise, long-form answers that are accurate and reliable.

3: FinGPT for Financial Sentiment Analysis

FinGPT has been fine-tuned using the Low-Rank Adaption (LoRA) method on datasets focused on news and tweets sentiment analysis. Notably, FinGPT achieved impressive results performance in financial sentiment metrics like FPB, making it a credible choice for finance businesses.

The model provides insights by analyzing sentiment trends from vast amounts of financial data, helping businesses understand market sentiments and make informed decisions.

Fine-Tuning Techniques

There is a whole range of different techniques used in fine-tuning, but we will focus on five in particular: transfer learning, feature extraction, regularization techniques, top-layer tuning, and learning rate adjustments.

Transfer Learning

Transfer learning is a technique in which a model developed for one task is reused as a starting point for another task. This approach benefits businesses by saving time and resources by not having to develop the model from scratch.

A financial firm might use transfer learning to adapt a model initially trained on broad economic data to predict stock performance specifically, reducing development time and resources while quickly adapting to market changes.

Feature Extraction

This technique involves processing data using the earlier layers of a neural network, capitalizing on these layers’ ability to detect universal input features (like basic syntax in text). The deeper layers are then fine-tuned to specialize in features specific to the new task.

It’s an efficient method that preserves a lot of the model’s learned capabilities while adapting it to new functionalities. For instance, hospitals can use models originally trained on general health data to detect specific pathologies in X-rays, boosting diagnosis accuracy without collecting large amounts of data. 

Regularization Strategies

Regularization strategies like dropout or weight decay prevent a model from overfitting during fine-tuning. Overfitting happens when a model learns the detail and noise to the extent that it negatively impacts the performance of the model on new data.

This is where regularization comes in - it makes slight adjustments to the learning process to keep the model generalized. Insurance companies can use this to prevent overfitting historical claims data so the model accurately predicts future claims in different situations.

Top-Layer Tuning

Top-layer tuning focuses on adjusting only the top layers of a neural network. It’s used when the new tasks are closely related to what the model originally learned. Notably, fine-tuning the output layer is especially important, as it often requires specific adjustments to ensure the neural networks perform well at new tasks.

Since the top layers of the network typically learn more specific features, fine-tuning them can quickly adapt the model to new but related tasks without extensive retraining.

Banks can fine-tune the top layers of a pre-trained model to adapt to the specific linguistic nuances found in mergers and acquisition reports. As a result, the model will be better at extracting critical deal points that inform investment strategies.

Learning Rate Adjustments

The learning rate is essential for balancing between retaining what the model has already learned and adapting to new data. A lower learning rate ensures the model’s weights aren’t drastically altered, which helps maintain stability in the model’s performance across old and new tasks. 

Adjusting the learning rate is also important for a trained network since it ensures the fine-tuning process refines the model’s capabilities without deviating too far from the foundational patterns it learned. It improves the accuracy of optimization algorithm resulting in higher quality results.

One example of this is that insurance companies can adjust the learning rate when fine-tuning models to predict customer churn based on policy renewal data.

Challenges in Fine-Tuning

Fine-tuning has some challenges that can make it difficult for your business to fully benefit from your fine-tuned deep learning models. These include data scarcity, overfitting, and ethical considerations.

1: Data Scarcity and Quality 

The fine-tuning process depends on quality training examples, which can be limited in specialized fields. Businesses can use techniques like synthetic data generation, where artificial data points are created to supplement the training datasets.

A healthcare company might use synthetic data generation to enhance its datasets and fine-tune models that predict the efficiency of new drugs when real-world clinical trial data is limited.

2: Overfitting

A model might perform well on training data but badly on unseen data, especially if insufficient training data is available. 

Some solutions to handle this problem include:

• Regularization techniques
• Cross-validation
• Data Augmentation

Moreover, continuously monitoring model performance on new data can help identify overfitting early.

3: Ethical and Bias Considerations

Another issue is that models can reflect or amplify biases in the training data, leading to unethical outputs. The risk of this occurring can be reduced by conducting thorough bias audits and using de-biasing techniques during model training. It’s also important to think about using diverse datasets and fairness criteria during model evaluation.

Using Fine-Tuning in Your Business

Fine-tuning deep learning models provides immense benefits like lower costs and higher quality outputs, but it also comes with its own set of challenges.

Challenges like overfitting and data scarcity are tricky to overcome, but they are highly rewarding. With a model specialized for a specific task, your business will have a massive competitive edge. 

If you need any help dealing with these challenges when using fine-tuning for deep learning models, schedule a free 30-minute consultation with us to see how we can help. 

FAQs

Can fine-tuning be used for any type of neural network?

Yes, fine-tuning applies to all types of neural networks and is widely used across various industries, including finance, healthcare, and insurance. For example, in healthcare, a neural network initially trained to recognize a wide range of medical images can be fine-tuned to specialize in detecting specific types of diseases.

What is the role of a small dataset in fine-tuning?

A small dataset is essential when the goal is to adapt a model to very specific or narrow tasks. For example, an insurance company might fine-tune a model on a small dataset consisting of specific types of claims to improve its ability to automate claims processing. This approach lets the model perform well on specialized tasks even with limited data.

Can fine-tuning be performed on any pre-trained model?

Fine-tuning can be performed on any pre-trained model, provided the model’s architecture is compatible with the new task. For instance, a model pre-trained on broad economic data might be fine-tuned to predict specific market trends or detect fraudulent activities unique to a particular company or sector.