Large Language Models

Delve into the world of Large Language Models (LLMs) with this comprehensive guide. From planning a vacation in a foreign country to revolutionizing industries like education and healthcare, discover how LLMs are reshaping the way we communicate and access information. Explore the myriad applications of these powerful tools while also understanding their potential drawbacks. Whether you're new to the concept or seeking a deeper understanding, this article provides a balanced perspective on the potential and pitfalls of LLMs in today's digital age.

By Paschal Alaemezie

Imagine that you are planning a vacation to a foreign country, but you don’t speak their language. Also, during your vacation, you want to book a hotel, find a restaurant, visit a museum, and enjoy the culture of this foreign country. How do you communicate with the locals and get the information you need? You could use a traditional translation app, but it might not understand the context or the nuances of the conversation. You could hire a human translator, but it might be expensive or inconvenient. What if there is a better way?

What if there is a smart app that can not only translate your words, but also understand your intentions, preferences, and emotions? What if it can also generate helpful suggestions, such as the best places to visit, the best dishes to try, or the best deals to get? What if it can also chat with you like a friend, and tell you stories, jokes, facts, or opinions about anything you want to know?

This is not science fiction. This is possible with large language models (LLM). Large language models are computer programs that can recognize, summarize, translate, predict, and generate text and other forms of content based on knowledge gained from massive datasets. They are behind some of the amazing features that we use every day, such as Siri, Cortana, Google Translate, Bing, etc. They are also creating new possibilities for education, healthcare, business, entertainment, and more.

In this article, you will learn:

  • What are large language models and how do they work
  • What are some real-life applications of large language models
  • What are some advantages and disadvantages of using large language models

So, if you are curious about this exciting technology and how it can change your life, grab a bowl of popcorn and read on!

What are Large Language Models
To understand what a large language model is, let’s first break down the term into its components:

  • Language model: A language model is a mathematical representation of how words and sentences are used in a natural language, such as English, Chinese, Arabic, etc. A language model can assign a probability to any sequence of words or characters, based on how likely it is to occur in the real world. For example, the sentence “I love chocolate” is more probable than the sentence “I love broccoli”, according to most language models. A language model can also predict the next word or character in a sequence, based on the previous ones. For example, given the word “The sky is”, a language model might predict “blue” as the most likely next word.
  • Large: A large language model is a language model that has many parameters (tens of millions to billions), trained on large quantities of unlabelled text (text that does not have any labels or annotations) using self-supervised learning or semi-supervised learning. Self-supervised learning means that the model learns from its input data, without any human guidance. Semi-supervised learning means that the model learns from a combination of labelled and unlabelled data. For example, a large language model might learn from millions of books, articles, websites, social media posts, etc., without knowing what they are about or who wrote them.
  • Model: A model is a simplified representation of a complex system or phenomenon, such as weather, physics, biology, etc. A model can be used to understand how the system works, to make predictions about its future behaviour, or to generate new examples of its output. A model can be expressed in different ways, such as equations, diagrams, graphs, etc. A large language model is expressed as an artificial neural network (ANN), which is a type of computer program that mimics how the human brain processes information.

To explain how a large language model works to a beginner in tech, let’s use an analogy like this:

Imagine that you have a very smart friend who loves reading books. He reads so many books that he knows almost everything about anything. He can tell you stories about different places, people, animals, things, etc. He can also answer your questions about anything you want to know. He can even write new stories for you based on what you like.

Now imagine that your friend is not a human being but a computer program. That program is called a large language model. It reads so many texts from the internet that it knows almost everything about anything. It can understand what you type or say and give you helpful answers or suggestions. It can also generate new texts for you based on what you want.

Real-Life Applications of Large Language Models
Large language models have many real-life applications in different domains and industries. Here are some examples:

  • Education and research: Large language models can help students and researchers learn new concepts, find relevant information, write essays or papers, check grammar and spelling errors, etc. For example, OpenAI’s GPT-3 can generate summaries of academic papers, Microsoft’s Turing-NLG can write natural language explanations for math problems, Google’s LaMDA can engage in open-ended conversations on any topic, etc.
  • Healthcare and medicine: Large language models can help doctors and patients diagnose diseases, prescribe treatments, monitor health conditions, provide medical advice, etc. For example, IBM’s Watson can analyse medical records and literature to provide evidence-based recommendations, Alibaba’s DAMO Academy can generate radiology reports from CT scans, Amazon’s Alexa can answer health-related questions and connect users to telehealth services, etc.
  • Business and commerce: Large language models can help businesses and customers communicate better, market products or services, analyse customer feedback, generate sales leads, etc. For example, Salesforce’s Einstein can write personalized email subject lines and content, Facebook’s BlenderBot can chat with customers and provide product recommendations, Microsoft’s CaptionBot can generate captions for images, etc.
  • Entertainment and media: Large language models can help creators and consumers generate or consume content, such as stories, poems, songs, jokes, games, etc. For example, OpenAI’s Jukebox can create music in different genres and styles, Google’s Verse by Verse can write poems in collaboration with famous poets, Microsoft’s Xiaoice can write novels and host live shows, etc.

Describing the advantages and disadvantages of Large Language Models
Large language models have many advantages and disadvantages. Here are some of them:


  • Versatility: Large language models can perform well at a wide variety of tasks, such as natural language understanding, natural language generation, question answering, text summarization, machine translation, etc. This is because they are general-purpose models that learn from diverse and rich data sources, rather than being trained for one specific task.
  • Scalability: Large language models can improve their performance by increasing their size (number of parameters), their training data (amount and quality of text), and their computing power (amount and speed of hardware). This is because they follow simple statistical laws that relate these variables to each other. For example, doubling the model size or the data size can lead to a significant improvement in performance. This claim is further strengthened by Tom B. Brown et al. in their research paper “Language Models are Few-Shot Learners” where they showed that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches… and found that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans.[1]
  • Creativity: Large language models can generate novel and diverse texts that are coherent and fluent. This is because they learn from a large corpus of human-generated texts that contain various styles, tones, emotions, opinions, facts, etc. They can also combine different sources of information and knowledge to create new content. For example, they can write stories based on images or keywords, or write lyrics based on melodies or genres.


  • Cost: These models have obtained notable gains in accuracy across many NLP tasks. However, these accuracy improvements depend on the availability of exceptionally large computational resources that necessitate similarly substantial energy consumption. As a result, these models are costly to train and develop, both financially, due to the cost of hardware and electricity or cloud computing time, and environmentally, due to the carbon footprint required to fuel modern tensor processing hardware.[2]
  • Bias: Large language models may reflect or amplify the biases and prejudices that exist in the data they are trained on. These biases may affect the quality and fairness of their outputs and decisions. They may also harm the reputation and trustworthiness of the models and their users. For example, large language models may generate texts that are sexist, racist, homophobic, etc., or favour certain groups or perspectives over others.
  • Ethics: Large language models may pose ethical challenges and risks to society. They may affect the privacy and security of the data they use or produce. They may also affect the accountability and responsibility of the models and their users. They may influence the behaviour and cognition of the people who interact with them. They may also have unintended or unforeseen consequences that are hard to predict or control. For example, large language models may generate texts that are misleading, deceptive, harmful, etc., or manipulate people’s emotions or opinions.

Large language models are powerful and versatile tools that can understand and generate natural language and other forms of content. They have many applications in various domains and industries, such as education, healthcare, business, entertainment, and more. They can also enhance human creativity and communication. However, large language models also have some drawbacks and challenges, such as high cost, potential bias, ethical issues, etc. Therefore, it is important to use them responsibly and wisely and to keep learning and improving them. Large language models are not only a fascinating technology, but also a window to the future of artificial intelligence and human society.

To recap, in this article, you learned:

  • What large language models are, and how they work.
  • Some real-life applications of large language models.
  • Some advantages and disadvantages of using large language models.

If you want to learn more about large language models, you can:

  • Read more articles or books on this topic. Books like Quick Start Guide to Large Language Models: Strategies and Best Practices for using ChatGPT and Other LLMs by Sinan Ozdemir, publisher by Addison-Wesley Professional with ISBN: 9780138199425.
  • Watch some podcasts on this topic. Podcasts like Richard Ngo on large language models, OpenAI, and Striving to Make the Future Go Well by 80,000 Hours, which explore the capabilities, challenges, and risks of LLMs, as well as the work that OpenAI is doing to shape their development and deployment.
  • Take some online courses. You can take this course at Coursera: Introduction to Large Language Models by Google Cloud, which introduces you to the concept of LLMs and their use cases. The course also explains how to use prompt tuning to enhance LLM performance, and how to use Google’s Gen AI development tools.

Thank you for reading this article! I hope you enjoyed it and learned something new!


  1. Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., … & Agarwal, S. (2020). Language models are few-shot learners. In Advances in Neural Information Processing Systems (Vol. 33).
  2. Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 3645-3650