Transfer Learning from Large Language Models

Written by Coursera Staff • Updated on

Learn how transfer learning from large language models (LLMs) makes it possible for computers to translate languages, summarize texts, and perform other human-like communication tasks easier, quicker, and more accurately than before.

[Featured Image] Two colleagues discuss transfer learning from large language models during a video chat.

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 transfer learning used for?

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.

How transfer learning from LLM 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.

  1. 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.

  2. 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.

  3. 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.

  4. 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 understand better 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

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 to work with LLMs. 


  • Reduced training time

  • More efficient use of resources

  • More accurate output


  • Requires high-quality data free from user bias

  • Accidental bias transfer from existing training data

  • Concerns about data security

How to get started

If you're considering a career in transfer learning and language learning models, learn more about machine and deep learning. You can start by exploring fields—natural language processing, computer vision, and more—and narrowing 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.

Getting started on Coursera

Large language models are a form of machine learning, and you can start building your knowledge of this dynamic field through the Machine Learning Specialization from Stanford. Designed for beginners, this Specialization covers topics like artificial neural networks and decision trees and allows you to build models and train a neural network. If you're ready to explore transfer learning, check out Transfer Learning for NLP with TensorFlow Hub. In this intermediate-level course, you can implement transfer learning and fine-tune the models using real-world data. You can find these courses and more on Coursera.

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