LLMs work by analyzing massive datasets to intelligently complete thoughts, but how exactly do they achieve that?
Large Language Models (LLMs) are advanced artificial intelligence systems that use deep learning techniques and vast amounts of data to understand and generate human-like text.
Say you wanted to participate in the popular game show Jeopardy (it’s an American TV game show where contestants are given the answer and have to guess the question). To be on the show you need to know everything about anything. So you decide to dedicate every day for the next 3 years, reading everything on the internet. Which you quickly realize is harder than it originally appeared and a super huge investment of time. You also realize that there is a vast mix of information on the internet. Some of it is fact, some is opinion, and most is somewhere in between. Jeopardy is based on facts, so spending most of your time somewhere in between is not smart.
After lots of coffee, you decide to take a different approach to training for Jeopardy. Instead of trying to know everything about anything, you focus on how to predict the next word in a sentence. If someone says “Have a nice…”, your training teaches you that the next word is probably going to be “day.” It’s an entirely different way to approach Jeopardy training, but could be your edge to nailing that daily double!
So you focus on the English language. You want to read all the sentences that have been written, to (hopefully) discover patterns. Then you use those patterns to predict the next word, when someone offers you a thought. What kind of data do you need to train with this new approach? How are you going to remember all the patterns?
This is the challenge a large language model (LLM) can solve. They are large because they have been trained across a very large set of data (like all the public content on the internet). They are a language model because they can use that large set of training data to understand how to complete a sentence in a given language (like English, Spanish, or French). And because information on the internet covers such a huge range of opinions, dialects, ideas, etc the models are very good at inferring patterns from the questions they’re asked.
Large language models are a form of artificial intelligence (AI). They have been trained on huge amounts of data and can intelligently complete a thought similar to the way a human would… They are artificially intelligent.
Large language models like GPT-3 represent a transformative force in the realm of artificial intelligence. Their emergence signals a paradigm shift in how machines understand and interact with human language. These models, trained on vast datasets, have acquired an unprecedented ability to comprehend, predict, and generate text in ways that closely mimic human cognition. This advancement is not just a technical feat but a gateway to new possibilities in human-computer interaction.
The impact of LLMs extends beyond mere language processing. They are reshaping industries, revolutionizing content creation, and altering the landscape of digital communication. By understanding context, nuances, and the subtleties of language, LLMs offer solutions that are both innovative and practical. They bridge the gap between the vast information available digitally and the human ability to process it, making them indispensable in the current era of information overload.
Here are key reasons for the importance of large language models:
LLMs can comprehend and generate text with a level of sophistication that mimics human language, making digital interactions more natural and effective.
These models are versatile, and capable of performing various tasks like content generation, language translation, summarizing information, and powering advanced chatbots.
LLMs have the potential to revolutionize content creation, providing new ways to generate creative and contextually relevant material.
With the ability to make sense of large datasets, LLMs can offer predictions and insights from minimal inputs, enhancing decision-making processes in various fields.
Their ability to process and analyze vast quantities of data surpasses traditional methods, enabling efficient handling of complex and extensive datasets.
LLMs aid in breaking language barriers, offering translation and localization services that enhance global communication.
The integration capabilities of LLMs with various APIs allow for tailored applications across different sectors, showcasing their adaptability and scalability.
To grasp the essence of Large Language Models (LLMs), it's crucial to dissect the core components that drive their functionality. These components, intricately woven together, form the backbone of LLMs, enabling them to emulate human language processing and generation with an unprecedented level of sophistication. Each element plays a distinct and pivotal role, from the foundational data they're trained on to the complex algorithms that guide their learning and adaptation.
This is the foundation of LLMs. The data comprises a wide array of text sources, enabling the model to learn language patterns, context, and nuances. The diversity and quality of this data significantly influence the model's performance and biases.
Neural Network Architecture:
LLMs often utilize advanced neural network structures, with the Transformer model being a common choice. This architecture is adept at handling sequential data and is fundamental for processing language efficiently.
Learning Algorithms:
These algorithms dictate how the model learns from the data. They involve complex deep-learning techniques, which enable the model to understand and generate language.
Computational Resources:
Training and operating LLMs require substantial computational power, often involving high-performance GPUs and extensive distributed computing systems. This is due to the massive scale of data processing and model complexity.
Fine-tuning Mechanisms:
After initial training, LLMs undergo fine-tuning to tailor them for specific tasks or to enhance their performance in certain areas. This process involves additional training focused on particular datasets or objectives.
When one goes about creating a large language model the first questions to answer are, what is the goal of the model, and how much data can you gather about the goal? LLMs like GPT have a pretty broad goal - complete any thought or idea. A model’s goal could be a bit more focused like indexing a very large set of documents to make them searchable. Large language models are “large” because their intended goal is typically a very big idea and the data needed to learn about the goal is vast. In fact for GPTs to complete any thought goal, the right amount of data might not exist to truly train it.
Almost every large language model has some nuance to it. Either differences in the data it was trained on, the way it was trained, optimizations to its learning paths, or how it goes about completing a thought. Compare Google’s Bard model to OpenAI’s GPT model. When you open a chat with either, things feel quite the same. You share a thought or ask a question and the model responds with something relevant to the conversation. But under the covers things are very different.
If your first interaction with a Large Language Model was using a website like ChatGPT, then you might be inclined to think LLMs are made to answer your questions. In fact, AI models don’t answer questions at all, they complete thoughts. Prompting a model with “It’s a lovely day.” versus “Is it a lovely day?” will get different responses. Not because one is a question and the other is a statement. To complete a thought a model tries to find the (statistically) best-fitting next set of words. Then the next set of words after that, and so on. The response is called a “completion” because the model is trying to figure out what comes next. To us, it sure feels like a question & answer.
A notable difference between many machine learning models and a large language model is that an LLM is based on a neural network. As the name suggests, a neural network simulates how a human’s neurons work. It’s a computational model trying to simulate human functions. As you can imagine this can get really confusing. To express how complicated an LLM is, you refer to the number of parameters in the billions. Very complicated. The needs of a smaller machine learning model typically don’t require the use of a neural network. This makes their complexity a little easier to handle but also limits their computational abilities.
Large language models (or really any machine learning system) aren’t instantly smart. Just like humans, they have to be taught (or trained) about a given topic. Training a model is very similar to how you and I would go about learning a topic.
Say we wanted to learn about donut recipes. What are the typical ingredients? What variants are there in making the dough? What kind of toppings can you put on a donut? (pretty much anything, right!) What recipes don’t make donuts?
To learn all this you would gather a bunch of recipes from sources you know are trustworthy. Then you would go about reading. A lot. Over time you would see patterns in all the recipes. Like most of them use flour. The ones that don’t use flour are usually considered gluten-free. This is called training data.
Donuts are typically topped with something sweet like sprinkles. You could use these common patterns to read other recipes and know if it’s for a donut. You would also notice that donuts are round with a hole in the middle. A recipe might call for similar ingredients to a donut but could be for making pancakes. You'll need to find consistent patterns to figure this out.
Training a large language model is very similar to this. The more recipe examples, the better the model can tell if a given recipe is to make a donut. You want a ton of recipes so that your model will be super good at identifying donut recipes.
Training a model to be able to determine if a recipe is for a donut is helpful but leaves quite a bit to be desired. Training models is not an easy task so you want to include as many features as possible. In this case, we might want the model to know what kind of donut is being made.
As you give the model all those donut recipes you can include the type of donut with each recipe. This is called data labeling. Using this approach means not only can the model determine if a recipe is to make a donut, but it can also answer what kind of donut is being made! Now someone can ask your model “Does this recipe make a chocolate donut?” Your model was trained with donut recipes that were labeled with the type, so it should be able to provide a very accurate answer.
Large language models come in many shapes and sizes. However, because large language models are so complicated and need huge amounts of data to train on, their designed goal is broad. Imagine creating a model to take 5 seconds of any song in the world and identify its artist. That’s not an easy task and requires knowledge of every song ever made.
Say you wanted to create a model that could identify if a given song was on a specific album. A large language model would not do well with this because you don’t have to train it on all the songs in the world. All it has to know about are the few songs in that album. That's not enough data to provide an accurate response. There are a ton of songs in the world that sound kinda similar to the songs on the album.
Large language models are meant to complete very abstract thoughts, with little context. Like “why did the chicken cross the road?” They are also meant to provide precise accurate answers when given clear examples and descriptions of what is desired. To be good at both these uses, it needs a huge amount of data for learning.
LLMs have a broad spectrum of applications, significantly impacting various fields:
In the realm of digital marketing and journalism, LLMs are transforming content creation. They assist in drafting articles, blogs, and even creative stories, enhancing the speed and diversity of content production. This is particularly beneficial for maintaining a constant stream of engaging and relevant material in content-heavy industries.
LLMs are redefining customer interaction by powering sophisticated chatbots and virtual assistants. These AI-driven tools can handle a multitude of customer queries in real-time, offering personalized and accurate responses, thus improving customer experience and operational efficiency.
The ability of LLMs to provide quick and accurate translations is breaking down language barriers in global communication. This application is invaluable in international business and travel, allowing for smoother cross-cultural interactions and transactions.
In education, LLMs contribute to personalized learning experiences and the creation of adaptive learning materials. They can simplify complex concepts, answer student inquiries, and even assist in language learning, making education more accessible and tailored to individual needs.
These models excel in analyzing and interpreting large volumes of text data, extracting key insights. This capability is crucial in market research, business intelligence, and scientific research, where understanding trends and patterns in vast datasets is essential.
LLMs play a significant role in developing tools for people with disabilities. For example, they can convert text to speech or provide descriptive text for visual content, enhancing accessibility in digital platforms.
The applications of LLMs are as diverse as they are impactful, demonstrating their potential to revolutionize various aspects of our personal and professional lives.
As of this document’s publish date here are a few examples of publicly available Large Language Models. We’ve tried to provide some context about the goals of each model and how to get started with them.
All of these models are natural language processing (NLP) models, meaning they have been trained to work with how a Human speaks (letters, words, sentences, etc).
This LLM was released in 2020 by OpenAI. It is classified as a generative large language model with around 175 billion parameters. OpenAI used a few different datasets to train GPT about the entire internet, with the biggest being Common Crawl.
GPT’s objectives are about continuing a provided thought. The thought could be complete like “it’s a great day” or could be a question like “why did the chicken cross the road”. GTP reads the text left-to-right and tries to predict the next few words.
Google released this LLM in 2018. It is based on the transformer architecture. BERT takes a different approach than GPT where it reads text both from the left and from the right to then predict the next few words. This gives the model a better understanding of the context of words.
This model was introduced by Facebook AI in 2019. It is based on Google’s BERT model with improvements to the performance and robustness of the original. The improvements focus on fine-tuning the pretraining process and training on a larger corpus of text data.
Introduced by Google Research in a paper published in 2019, the T5 model is designed to approach all natural language processing (NLP) tasks in a unified manner. It does this by casting all NLP tasks as a text-to-text problem. Both input and output are treated as text strings. This expands the abilities of the model including text classification, translation, summarization, question-answering, and more.
Created by Salesforce Research in a research paper published in 2019, this model is designed to generate text conditioned on specific instructions or control codes, allowing fine-grained control over the language generation process. It uses control codes to condition the language model's output. The codes act as instructions for the model during text generation. The control codes guide the model to produce text in a particular style, genre, or with specific attributes. This enables fine-tuned customization of the language generation process according to user-specified constraints.
This model is a combination of Microsoft’s DeepSpeed deep learning optimization library and NVIDIA’s Megatron-LM large transformer model. At the time of release it claimed the “world’s largest transformer-based language model” title, with 530 billion parameters (significantly more than GPT-3). Its massive size of parameters made the model quite good at zero, one, and few-shot prompts. It set a new bar in terms of scale and quality in modern LLMs.
The potential benefits of LLMs are vast and varied, offering transformative advantages across multiple domains and enhancing user interactions with technology. Here are a few of the benefits that we are seeing from use today:
LLMs provide information in an easily understandable, conversational style, enhancing user comprehension and engagement.
These models are versatile, used for language translation, sentiment analysis, question answering, and more, demonstrating their broad utility.
The performance of LLMs improves with additional data and parameters. They exhibit "in-context learning," enabling them to adapt and learn from new prompts efficiently.
LLMs learn quickly through in-context learning, requiring fewer examples and less additional training, demonstrating their efficiency in adapting to new tasks.
LLMs contribute to creative processes such as writing, art generation, and idea development, pushing the boundaries of AI-assisted creativity and innovation.
LLMs excel in tailoring content and interactions to individual user preferences and behaviors, significantly enhancing the personalization aspect in applications like digital marketing, e-learning, and customer service.
Large language models, while impressive in their capabilities, are not without their challenges. Despite their advanced technology and apparent understanding of language, these models are still tools with inherent limitations. These challenges range from technical and ethical issues to practical limitations in real-world applications.
LLMs are trained on existing data, which may contain biases. This can lead to biased outputs, reinforcing stereotypes or unfair representations. Addressing these biases is crucial to ensure fair and ethical AI applications.
The training of LLMs demands significant computational power, often requiring advanced GPUs and substantial electricity. This not only leads to high costs but also raises environmental concerns due to the carbon footprint associated with energy use.
LLMs process large volumes of data, including potentially sensitive information. Ensuring the privacy and security of this data is a major challenge, necessitating robust data protection measures to prevent breaches and misuse.
Over-reliance on LLMs for tasks like writing and decision-making could result in a decline in related human skills. There's a risk of becoming too dependent on AI, which might affect critical thinking and problem-solving abilities.
Tailoring LLMs for specific needs or industries can be intricate and resource-intensive. It requires deep expertise not only in machine learning but also in the domain of application, which can be a barrier for many organizations.
While LLMs have advanced significantly, they still struggle with understanding context and subtleties in language. This can lead to inaccuracies or inappropriate responses, especially in complex or nuanced situations.
As we look ahead, the landscape of Large Language Models (LLMs) is ripe for groundbreaking developments and trends. The next wave of these models is poised to be more efficient and environmentally sustainable, addressing the current concerns regarding their resource-intensive nature. Innovations are being directed towards reducing computational requirements while maintaining, or even enhancing their performance capabilities. This evolution is crucial for making LLMs both more accessible and environmentally friendly.
In parallel, there is a growing emphasis on creating ethically sound LLMs. With a heightened awareness of inherent biases in AI, efforts are intensifying to develop models that are impartial and equitable. This involves a nuanced approach to training LLMs, ensuring a diverse and inclusive dataset. Additionally, the future is likely to see LLMs tailored more specifically to individual industries, providing bespoke solutions for unique challenges. Their integration with other cutting-edge technologies, such as blockchain and augmented reality, is expected to unlock new possibilities in user interaction and technology applications. These advancements will continue to expand the horizons of human-machine collaboration.
Once you set the goal for your Generative AI project, you can select an LLM that best fits the need. Most likely the LLM offers an API to interact with it (ie: submit prompts and receive responses). You’ll want the prompts to be a balance between project goals and the LLM’s characteristics. That balance is going to include additional information that the LLM has no knowledge of. Learn more about prompt engineering.
Typically you use a vector database to match a User’s input with your pre-made text, to create a perfectly crafted prompt. This will ensure the LLM’s responses are predictable and stable enough to include in your larger efforts. At its simplest, the flow will be:
While this may sound complex, Datastax Astra takes care of most of this for you with a fully integrated solution that provides all of the pieces you need for contextual data. From the nervous system built on data pipelines to embeddings all the way to core memory storage and retrieval, access, and processing in an easy-to-use cloud platform.
A large language model is a form of AI trained on extensive data, enabling it to complete sentences or thoughts in various languages. Its large training dataset, often encompassing internet content, allows it to infer patterns effectively.
LLMs work by utilizing vast datasets to complete thoughts or ideas, adapting to the given context. The goal of the model and the amount of data available play a significant role in its functioning.
They are termed "large" due to the enormous amount of data they are trained on, which is critical for achieving their broad goal of understanding and completing thoughts in a given context.
Unlike many machine learning models, LLMs are based on neural networks, simulating human neuronal functions, which allows for a higher level of computational abilities, albeit with increased complexity.
Training involves feeding the model a plethora of data, similar to how humans learn by reading and identifying patterns. This training enhances the model's ability to identify and complete thoughts accurately.
LLMs don't answer questions per se; they complete thoughts. The way a prompt is structured influences the model's response, which aims to provide a coherent completion of the given thought.
