Transfer Learning from Large Language Models

Key takeaways

Transfer learning in large language models (LLMs) is when the model takes what it learned during training and applies it to new problems.

• Businesses use transfer learning from LLMs to design chatbots, analyze customer sentiment, create forecasts, predict trends, and automate tasks.

• These models learn by analyzing patterns in the presented data and applying them to specific tasks, repeating the process until they can reliably predict and produce language that makes sense. 

• You can use transfer learning from LLMs in industries such as marketing, customer service, health care, and education. 

Learn more about transfer learning from LLMs by exploring what exactly transfer learning is, how it works, what it’s used for, and careers that benefit from this technique. To continue learning, consider completing the Machine Learning Specialization by Stanford University and DeepLearning.AI. In as little as two months, you can learn how to apply best practices for machine learning development, including both supervised and unsupervised learning techniques. By the end of the course, you’ll have had the opportunity to put your skills into practice and build a recommender system with a collaborative filtering approach and a content-based deep learning method.

What is transfer learning from large language models?

Transfer learning in large language models (LLMs) is the process LLMs use to apply their prior knowledge to new tasks. As a form of artificial intelligence, LLMs can process a tremendous amount of data, and they use deep learning to access that data to generate and predict human language.

These models learn by analyzing patterns in the presented data and applying them to specific tasks, repeating the process until they can reliably predict and produce language that makes sense. Transfer learning allows the LLM to apply its existing knowledge to a new task to work more efficiently and accurately.

What is an example of a transfer learning model? LLM use cases

You can use transfer learning when you want an LLM to work more efficiently, including tasks like generating text, engaging in conversation, and analyzing customer sentiment. The process helps improve workflow by automating activities within the workplace, freeing up human staff so they can work more productively on complex tasks and create content. Businesses use LLMs in the following applications, and transfer learning helps the models perform better.

• Customer sentiment analysis to help you understand their behaviors and interests

• Chatbots to assist customers

• Content to communicate with customers, share information, and market products and services

• Detecting tumors and understanding diseases

• Research, including summarizing and translating texts

• Software development, including writing code, creating video games, and developing simulations

Read more: 10 LLM Use Cases to Enhance Your Business 

Transfer learning vs. few-shot learning: What’s the difference? 

While transfer learning is a machine learning approach that uses knowledge acquired from one data set to improve a model’s performance with a new data set, few-shot learning is a machine learning framework in which a model gains applicable information from a limited number of examples. However, transfer learning usually functions as the foundation from which most few-shot learning techniques are built. Similar to transfer learning, you can also use few-shot learning to adapt pre-trained models for similar tasks, and it ​​can aid in training generative models for classification tasks when adequate training data is lacking.

How transfer learning from large language models works

Transfer learning is a multi-step process that starts with identifying the purpose of the task and ends with fine-tuning the model. The following steps detail what happens in each phase.

• Identify use cases: Consider the tasks that can help your operation work more efficiently and how you can improve the customer experience with an LLM. With this information, you can select a pre-trained language model that will work well with the specific task you want it to perform.

• Run the model: During this step, the system analyzes a large amount of data related to the task you want it to perform. As the neural network processes the data, it looks for patterns and learns features like syntax and context that provide the structure of language understanding.

• Fine-tune the model: The initial output from the LLM may need some fine-tuning to improve its accuracy. You should notice improved output and increased effectiveness as you refine the model's data set. The fine-tuning process ultimately saves time and resources.

• Keep up with changing data: Because of the fast pace of technological advancement, keeping the model up to date on new data, growing customer demands, and trends in the industry is important. As you monitor the model and its performance, look for features you can improve and stay updated on new models that improve your business needs.

Who uses transfer learning?

Businesses in various industries use transfer learning from LLMs to create forecasts, predict trends, and automate tasks. Some areas that rely on LLMs to run more effectively include:

• Marketing: Some businesses use LLMs to create marketing materials, such as advertising copy, blogs, and other content. Developers continue to improve existing LLMs to better understand how to align content with the company's brand and engage the target audience.

• Contact centers: LLMs offer customers human-like communication and personalized responses, which are key to good customer service. LLMs also allow customers to receive an immediate response from online customer support and chatbots, so they don’t have to wait for assistance.

• Health care: Using LLM software, health care professionals can more effectively organize patient data. It can also be helpful for medical imaging to detect abnormalities and in health care research, where it's used to study rare diseases or the effects of illnesses on subgroups.

• Education: An LLM can act as a tutor or teacher, delivering customized content for the learner. Educators can also use LLMs to differentiate lessons to meet the needs of students with different learning abilities and address the specific needs of students at their instructional level. 

Pros and cons of transfer learning from large language models

Large language models are useful for a variety of tasks, and transfer learning helps these models work more efficiently. At the same time, this process has some limitations to consider—the more you understand these pros and cons, the better prepared you will be to work with LLMs. 

Pros

• Reduced training time

• More efficient use of resources

• More accurate output

Cons

• Requires high-quality data free from user bias

• Accidental bias transfer from existing training data

• Concerns about data security

How to get started in transfer learning

If you're considering a career in transfer learning and language learning models, learn more about machine learning and deep learning. You can start by exploring fields such as natural language processing, computer vision, and others, and then narrow your interests. Another option is to hone your skills and build a portfolio by freelancing or volunteering. You may consider attending school since machine learning engineers typically have at least a bachelor's degree in computer science or a related field like mathematics or data science.

Many industries use transfer learning from large language models, and you can find opportunities to apply your knowledge and skills in various fields. These include contact centers, video gaming, e-commerce, health care, and law. If you know the area you'd like to work in, you may find it helpful to build knowledge of that field and choose related projects as you build your portfolio.

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