How do I start learning about LLM? A beginner’s guide to large language models

In the era of Artificial Intelligence (AI), Large Language Models (LLMs) are redefining our interaction with technology, work, and how we process and understand information. 

Performing complex language-related tasks, LLMs are offering new possibilities for many industries, like healthcare, retail, finance and law. Have you considered how LLMs could help you in your day-to-day tasks?

Let’s talk about how LLMs work, their functions and their challenges.

What are Large Language Models (LLMs)?

In short, LLMs are computer programs designed to understand and generate human text. These AI models are trained on vast amounts of text that allow them to understand and generate human language. 

LLMs can write text, summarize documents, translate, and automate responses, among other tasks. This technology can significantly reduce time and effort.

For example, Imagine that you have a successful e-commerce startup and receive hundreds of customer inquiries every day. People ask about order statuses, return policies, or product details. Responding to each message individually is time-consuming and requires a lot of staff. Using an LLM, you could develop a chatbot that automatically understands and answers these common questions.

Another example: Imagine you’ve collected hundreds of customer reviews. Reading and analyzing them manually would be overwhelming and time-consuming. With the help of an LLM, you can generate a comprehensive report summarizing the key takeaways, such as common complaints, frequently mentioned features, and overall customer satisfaction, in a fraction of the time.

How do LLMs write/generate text?

Here’s how an LLM learns: LLMs primarily use statistical techniques. During training, they are fed huge amounts of text. This enables them to predict the probability of the next words in a sequence based on the patterns and context observed in the texts on which they were trained. 

Fun fact: Did you know LLMs are metaphorically called “stochastic parrots”? They are “stochastic” (a system involving chance of probability) because an LLM’s selection of the next word or phrase it generates is based on a probabilistic process, and they are like “parrots” because they repeat text patterns they’ve seen during their training.

What are some of the functions of LLMs?

For individuals and companies, using a publicly available LLM or creating a private LLM can vastly improve the efficiency and effectiveness of many tasks:

  • Task Automation: LLMs can automate repetitive and time-consuming tasks, like generating reports, researching, and responding to inquiries.
  • Improved Customer Service: LLMs can significantly improve the customer experience by providing personalized communication that is available 24/7.
  • Data Analysis: LLMs can effectively analyze large volumes of text and provide strategic insights that can empower and guide in making informed decisions.

What are some of the challenges of AI?

Among the biggest challenges of AI is its high energy consumption—it takes a lot of power to run, and that power can be costly. LLMs, for example, contain billions of parameters that must be adjusted during training, requiring a large amount of computational resources and specialized hardware. 

Check out this video on the positive outlook of a respected Silicon Valley venture capitalist about the future of AI.

How can you start learning more about LLMs and how to use them?

Here are some essential starting points to start learning about LLMs and how they can help you with your individual or company’s tasks. 

  1. Understand your specific needs – In which areas can an LLM provide the most value? Do you need to automate report generation, improve customer service, analyze large volumes of textual data or something else?
  2. Understand the need for data cleaning, formatting and tokenization – To train LLMs, data should be clean and free from errors, in a suitable format for processing, and divided into smaller units of text, like words or subwords, that the LLM can understand.
  3. Understand the pros and cons of pre-trained models, open source models or your own LLM – Trained models are ready for instant use but don’t adapt to all needs. Open-source models give you greater flexibility to customize but require more technical knowledge and resources. Creating a model from scratch can offer you the most control and customization, but you need greater resources, time and machine-learning expertise.

Read our article on Leverage Hugging Face’s TGI to Create Large Language Models (LLMs) Inference APIs – Part 1.

Training LLMs and other AI applications

Implementing and training LLMs requires a team with experience in machine learning, natural language processing and data handling. It also requires a robust infrastructure and enough GPUs to conduct high-performance tasks. 

Massed Compute provides on-demand, scalable Cloud GPU services that enable you to train your own large language models (LLMs) without the need for an in-house setup team or physical hardware. Check out our marketplace for state-of-the-art Cloud GPU services with a broad selection of NVIDIA GPUs.