Large Language Models In A Nutshell

Large language models (LLMs) are AI tools that can read, summarize, and translate text. This enables them to predict words and craft sentences that reflect how humans write and speak.

Understanding large language models

Large language models have transformed natural language processing (NLP) because they have facilitated the development of powerful, pre-trained models for a variety of tasks. 

Large language models are trained on vast datasets with hundreds of millions (or even billions) of words. Complex algorithms recognize patterns at the word level and allow the model to learn about natural language and its contextual use.

LLMs such as GPT-2 and BERT have replaced a lack of in-house training data and the tedious feature extraction process with datasets that train large neural networks. These models rely on recurrent neural networks (RNNs) to parse the data and predict what words will come in next in a particular phrase or sentence. 

For example, if a model analyzed the sentence “He was riding a bicycle”, the LLM can understand what a bicycle is by analyzing swathes of data from words that tend to surround it. This makes them a powerful and versatile AI tool that provides accurate natural language generation, sentiment analysis, summarization, and even question-answering.

How are large language models trained?

Large language models are fed with text excerpts that have been partially obscured, or masked. The neural network endeavors to predict the missing parts and then compares the prediction to the actual text. 

The neural network performs this task repeatedly and adjusts parameters based on the results. Over time, it builds a mathematical model of how words appear next to each other in phrases and sentences.

Note that the larger the neural network, the greater the LLM’s capacity to learn. The LLM’s output is also dependent on the size and quality of the dataset. If the model is exposed to high-quality, well-curated text, it will be exposed to a more diverse and accurate array of word sequences and make better predictions.

Large language model examples

Turing NLG

Turing NLG is a 17-billion parameter LLM developed by Microsoft. When it was released in early 2020, it was the largest such model to date.

The model is a transformer-based generative language model. This means it can generate words to finish an incomplete sentence, answer questions with direct answers, and provide summaries of various input documents.


Gopher is a 280-billion-parameter model developed by DeepMind. Gopher was based on research into areas where the scale of the model boosted performance such as reading comprehension, fact-checking, and the identification of toxic results.

Research has discovered that Gopher excels in Massive Multitask Language Understanding (MMLU), a benchmark that covers model knowledge and problem-solving ability in 57 subjects across numerous STEM disciplines.


OpenAI’s GPT-3 is fed with around 570GB of text information sourced from the publicly available dataset known as CommonCrawl. 

With one of the largest neural networks ever released, GPT-3 can recreate anything that has a language structure. This includes answers to questions, essays, summaries, translations, memos, and computer code.

Key takeaways

  • Large language models (LLMs) are AI tools that can read, summarize, and translate text. They can predict words and craft sentences that reflect how humans write and speak.
  • Large language models are fed with text excerpts that have been partially obscured, or masked. The neural network then endeavors to predict the missing parts and then compares the prediction to the actual text.
  • Three popular and powerful large language models include Microsoft’s Turing NLG, DeepMind’s Gopher, and OpenAI’s GPT-3. 

Connected AI Concepts


Generalized AI consists of devices or systems that can handle all sorts of tasks on their own. The extension of generalized AI eventually led to the development of Machine learning. As an extension to AI, Machine Learning (ML) analyzes a series of computer algorithms to create a program that automates actions. Without explicitly programming actions, systems can learn and improve the overall experience. It explores large sets of data to find common patterns and formulate analytical models through learning.

Deep Learning vs. Machine Learning

Machine learning is a subset of artificial intelligence where algorithms parse data, learn from experience, and make better decisions in the future. Deep learning is a subset of machine learning where numerous algorithms are structured into layers to create artificial neural networks (ANNs). These networks can solve complex problems and allow the machine to train itself to perform a task.


DevOps refers to a series of practices performed to perform automated software development processes. It is a conjugation of the term “development” and “operations” to emphasize how functions integrate across IT teams. DevOps strategies promote seamless building, testing, and deployment of products. It aims to bridge a gap between development and operations teams to streamline the development altogether.


AIOps is the application of artificial intelligence to IT operations. It has become particularly useful for modern IT management in hybridized, distributed, and dynamic environments. AIOps has become a key operational component of modern digital-based organizations, built around software and algorithms.

Machine Learning Ops

Machine Learning Ops (MLOps) describes a suite of best practices that successfully help a business run artificial intelligence. It consists of the skills, workflows, and processes to create, run, and maintain machine learning models to help various operational processes within organizations.

OpenAI Organizational Structure

OpenAI is an artificial intelligence research laboratory that transitioned into a for-profit organization in 2019. The corporate structure is organized around two entities: OpenAI, Inc., which is a single-member Delaware LLC controlled by OpenAI non-profit, And OpenAI LP, which is a capped, for-profit organization. The OpenAI LP is governed by the board of OpenAI, Inc (the foundation), which acts as a General Partner. At the same time, Limited Partners comprise employees of the LP, some of the board members, and other investors like Reid Hoffman’s charitable foundation, Khosla Ventures, and Microsoft, the leading investor in the LP.

OpenAI Business Model

OpenAI has built the foundational layer of the AI industry. With large generative models like GPT-3 and DALL-E, OpenAI offers API access to businesses that want to develop applications on top of its foundational models while being able to plug these models into their products and customize these models with proprietary data and additional AI features. On the other hand, OpenAI also released ChatGPT, developing around a freemium model. Microsoft also commercializes opener products through its commercial partnership.


OpenAI and Microsoft partnered up from a commercial standpoint. The history of the partnership started in 2016 and consolidated in 2019, with Microsoft investing a billion dollars into the partnership. It’s now taking a leap forward, with Microsoft in talks to put $10 billion into this partnership. Microsoft, through OpenAI, is developing its Azure AI Supercomputer while enhancing its Azure Enterprise Platform and integrating OpenAI’s models into its business and consumer products (GitHub, Office, Bing).

Stability AI Business Model

Stability AI is the entity behind Stable Diffusion. Stability makes money from our AI products and from providing AI consulting services to businesses. Stability AI monetizes Stable Diffusion via DreamStudio’s APIs. While it also releases it open-source for anyone to download and use. Stability AI also makes money via enterprise services, where its core development team offers the chance to enterprise customers to service, scale, and customize Stable Diffusion or other large generative models to their needs.

Stability AI Ecosystem


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