Large Language Models, or LLMs for short, have become increasingly popular with the launch of platforms like OpenAI’s ChatGPT.
LLMs, also known as text generators or text prediction models, have revolutionized various industries with their ability to understand and generate natural language.
Chatbots, virtual assistants, content generators, code generators, and even simple question-and-answer models are being used by businesses across multiple industries.
In this article, we’ll explore what LLMs are, how they can impact your business, and how to get started with your first LLM.
What is an LLM?
LLMs, or large language models, are a special type of model based on foundational neural networks that fall under generative AI and have been trained on massive amounts of text data — typically on the order of petabytes.
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This text data is generally scraped from content on the internet including:
- Blog posts
- Publications
- Books
- Articles
- Websites
Once trained, the models can be used to handle many text-related tasks with human-like abilities, such as question/answer, translation, sentiment analysis, and much more.
This makes LLMs an integral part of our daily lives, being used in technology from virtual assistants to chatbots on websites and social platforms.
They are also being used in legal research to analyze and summarize large volumes of legal documents, in healthcare to assist with medical diagnoses, and in education to provide personalized tutoring and feedback to students.
How do LLMs work?
Large language models are based on transformer networks that learn patterns in text.
Like recurrent neural networks, transformers are built to learn sequential patterns but have three key components that make them even more powerful:
- Self-Attention: helps keep track of relationships of words that come before and after a given word. This is achieved by computing and optimizing attention weights during the training process. The calculated attention weights are a mathematical indication of the importance of each word in a sequence to every other word, which allows for the understanding of context.
- Positional Embeddings: help the model keep track of word order. This is achieved by utilizing a technique that encodes the position of each word in a sequence of text. What makes this technique unique is that it does not just index words, but instead calculates a matrix where each row is a vector that represents each encoded-word, allowing for the understanding of word order while avoiding large indices when dealing with large text sequences.
- Multi-Head Attention: Similar to self-attention, multi-head attention helps keep track of relationships between words. The difference, however, is that multi-head attention calculates additional sets of attention weights in parallel and then concatenates the results, allowing not only for a more complex and nuanced understanding of word relationships, but also faster training.
Once trained, the LLM becomes capable of generating text by predicting the most probable words or phrases given a prompt or content.
You can see why, then, basing LLMs on transformer networks makes them faster, more accurate, and able to understand more complex and nuanced word associations.
Closed Source vs. Open Source LLMs
While there are many LLMs available for developers and data scientists to interact with, there are two main categories in how those LLMs are managed: Closed Source and Open Source
Closed-Source LLMS
Closed-source LLMs are proprietary and developed by companies that retain full control over the underlying technology and the generated text. They do not share the source code or disclose training data to users.
Open-Source LLMs
Open-source LLMs, on the other hand, offer more transparency and are developed by organizations that share their source code, training data, and other relevant details.
These models are freely available to the public, allowing users to access, modify, and improve upon the model’s architecture and training technique.
Open-source LLMs, like GPT-3, have gained significant popularity due to the versatility and the ability for developers and data scientists to build applications on top of them.
Choosing Between Closed Source and Open Source LLMs
The choice between open-source and closed-source LLMs depends on several factors, including:
- User’s specific training needs
- Level of control and transparency
- Internal or integrated management
Organizations that do not have a preference for training data or are looking to quickly integrate LLM capabilities into their applications will more likely select a closed-source (or managed) LLM.
Organizations that want full control over training, tuning, and operating the LLMare more likely to select open-source LLMs.
LLM Applications and How They Impact Your Business
LLMs have proven to support complex business requirements by bringing AI into the mainstream and providing a long list of valuable capabilities.
An initial list of where LLMs can support your project needs is listed below:
How to Select LLMs for Evaluation
With so many awesome capabilities that can be supported by LLMs, users need to take time to evaluate which options support their project needs.
Were commend evaluating the following factors before working with a specific LLM:
- Open-Source vs. Closed-Source
Evaluate if your organization wants to utilize a Closed Source LLM, which typically has specific access options and cost structures, or an open-source model, which provides more flexibility, but requires more planning and operational investments.
- License
The license associated with the LLM is one of the most important areas to evaluate. If you select an LLM that has a license that does not align with your business, you won't be able to use features utilizing the LLM.
- Training Documents
Look at the documents used to train the model as this will impact the breadth and accuracy of the model. Quality and diversity of the data are crucial for training a robust LLM.
- Tokens
Tokens in LLMs are the basic units of text that the model reads and processes. It's important to understand tokenization when working with LLMs. The number of tokens in an input text can affect how much it costs to run a model, how long it takes to generate a response, and what is available to be included in the response.
Getting Started with Your First LLM
There are several ways to start interacting with LLMs, but we recommend these two:
OpenAI’s ChatGPT
ChatGPT is based on the GPT-3.5 architecture and is specifically designed and fine-tuned to excel in conversational tasks and interactions with human users, making it a specialized LLM for conversational questions.
With OpenAI’s ChatGPT, users can type simple questions into the model to get an answer, similar to asking a question of Apple’s Siri or Google’s Assistant.
Hugging Face
Hugging Face provides the Hugging Face Model Hub, where users can find and share pre-trained models, datasets, and other resources related to NLP.
Hugging Face has gained popularity among the AI, ML, and data science communities as a great place to publish, share, and interact with models. It provides a quick and free option to test models via an API or commercial options via AWS or Azure.
You can visit the Hugging Face community here.
Next Steps
After you have taken time to explore and interact with LLMs and you are ready to implement them into your applications.
We’ll cover more of this in future blog posts, but you’ll want to:
- Identify the optimal LLM for your use case
- Train (if needed) the LLM using your target data
- Deploy and test the LLM for accuracy and performance
- Fine-tune the LLM based on testing
- Deploy for scale and production operations
Deploying is often the hardest step for most developers and data scientists, but we’ve made it easy with our Inference Engine, which is part of the Ulap Machine Learning Workspace.
See how quickly you can deploy an LLM with our Inference Engine in this video or signup for a 30-day free trial to test it out yourself.
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