What Are Large Language Models?
Large language models (LLMs) are a type of artificial intelligence (AI) that uses machine learning algorithms to replicate human language. It uses massive data sets to develop its ability to translate languages, predict text, and generate content. As opposed to natural language processing models (NLPs), LLMs train on much larger data sets, allowing it to use a greater number of parameters to become more complex and closer to human language.
As LLMs become more complex and human-like, they raise more ethical questions about their diversity, energy requirements, ability to make decisions, and use as content creators.
What are large language models used for?
From generating content to creating the foundations for AI chatbots, LLMs have a range of uses.
Generating content: LLMs rewrite, summarize, and generate new text based on a prompt or topic it is familiar with.
Translation: With proper training, LLMs can translate between languages.
Chatbots: LLMs power chatbots like ChatGPT-4, Google PaLM, and Meta's LLaMA, all of which interact with users in a familiar dialogue style.
Categorizing text: LLMs classify text and organize it into specific categories.
LLMs have the power to perform any number of tasks related to the use of language and can even automate everyday language tasks.
How do large language models work?
At their core, LLMs are deep learning models based on neural networks, machine learning algorithms that attempt to replicate human neural activity. LLMs start by using tokens, which are words broken into numerical representations. To create the relationships between words in contextual examples, LLMs use vectors in three-dimensional space to create relationships and, thus, sentences by decoding and recoding meaning. Sentences form through the selection of tokens based on statistics performed during its training.
LLMs often use unsupervised learning and unstructured data to access mass quantities of data. After the initial training, models undergo "fine-tuning" if they require specific use cases by prompting specific bits of data.
Who uses large language models?
Various industries use LLMs to create unique customer experiences with chatbots, support scientific research in classification, and easily create meeting transcripts. LLMs can also help marketing teams organize customer feedback and see how their audience talks about their brand through sentiment analysis.
How to get started with large language models
You can start interacting with large language models like ChatGPT from OpenAI or Google Bard to learn how they interact with you. Each chatbot interacts differently. ChatGPT tries to function like a regular conversation by guessing answers to the question without asking for more information. However, Google Bard focuses on search prompts, giving lists of answers and why it gave them in relation to your initial question, getting more focused on each question.
Many companies provide a baseline LLM architecture with a framework already in place to create a fine-tuned, customizable agent for your organization. When building an LLM you can use retrieval augmented generation (RAG) as a way to turn your information into a vector database that the LLM pulls from to create responses. A problematic factor in creating an LLM is the number of parameters, which is why many companies use existing frameworks that use their own data as well as the model's training.
Via: www.coursera.org
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