Large Language Models (LLMs): Overview

A large language model (LLM) is a deep learning algorithm that’s equipped to summarize, translate, predict, and generate text to convey ideas and concepts. Large language models rely on substantively large datasets to perform those functions. These datasets can include 100 million or more parameters, each of which represents a variable that the language model uses to infer new content.

Large language models utilize transfer learning, which allows them to take knowledge acquired from completing one task and apply it to a different but related task. These models are designed to solve commonly encountered language problems, which can include answering questions, classifying text, summarizing written documents, and generating text.

In terms of their application, large language models can be adapted for use across a wide range of industries and fields. They’re most closely associated with generative artificial intelligence ♋(generative AI).

How Large Language Models Work

Large language models work by analyzing vast amounts of data and learning to recognize patterns within that data as they relate to language. The type of data that can be “fed” to a large language model can include books, pages pulled from websites, newspaper articles, and other w🤡ritten documents that are human language–based.

In terms of the mechanics of large language models, there are some key steps that must occur for them to wo🍸rk:

• A large language model needs to be trained using a large dataset, which can include structured or unstructured data.
• Once initial pre-training is complete, the LLM can be fine-tuned, which may involve labeling data points to encourage more precise recognition of different concepts and meanings.
• In the next phase, deep learning occurs as the large language model begins to make connections between words and concepts. Deep learning is a subset of artificial intelligence that is designed to mimic how the human brain processes data. With extensive, proper training, deep learning uses a neural network that makes inferences from unstructured data to analyze information and solve problems.
• Once the model is trained, it should then be equipped to produce language-based responses using specific prompts.
  

A large language model operates as a type of transformer model. Transformer models study relationships in sequential datasets to learn the meaning and context of the individual data points. In the case of a large language model, the data points are words. Transformer models are often referred to as foundational models because of the vast potential they have to be adapted to different tasks and applications that utilize AI. This includes real-time translation of text and speech, detecting trends for fraud prevention, and online re꧂commendatౠions.

Types of Large Language Models

There are several types of large language models in use. The differences between them lie largely in how they’re trained and how they’rཧe used. Here’s how they compare at🍎 a glance.

• Zero-shot model: Zero-shot models are generalized large language learning models that are trained using a wide body of data to generate answers to questions. These models generally don’t require any additional training for use.
• Fine-tuned or domain-specific models: When a zero-shot model is subject to additional training, the end result can be a fine-tuned model. Fine-tuned models are typically smaller than their zero-shot counterparts, as they’re designed to handle more specialized problems. OpenAI’s Codex is an example of a fine-tuned model that’s more refined than its zero-shot model predecessor, GPT-3, that generates code. With a domain specific to finance, BloombergGPT is a model that performs financial tasks.
• Edge or on-device models: Edge models can operate like fine-tuned models, but they typically have an even smaller scope. This type of model is often designed to produce immediate feedback based on user input. Google Translate is an example of an edge model at work.

In addition to GPT-3 and OpenAI’s Codex, other examples of large language models include GPT-4, LLaMA (developed by Meta), and BERT, which is short for Bidirectional Encoder Representations from Transformers. BERT is considered to be a language representation model, as it uses deep learning that is suited for natural language processing (NLP). GPT-4, meanwhile, can be classified as a multimodal model, since it’s equipped to recognize and generate both text and images.

What Are Large Language Models Used For?

Large language models have a broad range of capabilities, and there are numerous ways in which t🅘hey can be used. There are five specific categories of activities in which LLMs may be employed:

• New content generation
• Summarization of existing content
• Translation across languages, or from text to code
• Classification of texts
• Chatbot applications

AI and large language models are increasingly being used in various 澳洲幸运5开奖号码历史查询:industries, ranging from finance to health care to marketing. Some specific example𒐪s of uses for large language models include:

• Training LLMs to analyze medical records or research studies, in order to identify patterns or make predictions about outcomes relating to specific health treatments or conditions.
• Utilizing large language models to power chatbot applications to provide customer service and reduce the need for human employees.
• Using LLMs to write email newsletters, video scripts, blog articles, and social media posts in order to streamline the content creation process.
• Training large language models to write software programs or create code for mobile applications.
• Incorporating LLMs into online search engines to provide the most accurate results to consumers who are searching for a specific topic, keyword, or query.

Those are just some of the ways that large language models can be and are being used. While LLMs are met with skepticism in certain circles, they’re being embraced in others.

LLMs vs. Artificial Intelligence

In short, LLMs are a type of AI-focused specifically on understanding and generating human language. LLMs are AI systems designed to work with language, making them powerful tools for processing and creating text.

Artificial intelligence is a more broad field that encompasses a wide range of technologies aimed at mimicking human intelligence. This includes not only language-focused models like LLMs but also systems that can recognize images, make decisions, control robots, and more. AI covers many fields such as computer vision, robotics, and machine learning. While LLMs are a part of AI,💛 the field of AI as a whole is much broader.

One example would be to contrast OpenAI products like ChatGPT and Sora against each other. An LLM like ChatGPT is great at generating text that sounds human-like and understanding complex language patterns. Other AI systems like Sora have visual patches that generate videos from text prompts, meaning it is not confined to the "language" or text medium.

Supervised vs. Unsupervised LLM Learning

Supervised and unsupervised learning are two approaches to training LLMs. Supervised learning ꧑involves training a model on a labeled dataset where each input comes with a corresponding output called a label. For example, a pre-trained LLM might be fine-tuned on a dataset of question-and-answer pairs where the questions are the inputs and the answers are the labels. In a supervised learning environment, a model is fed both the question and answer.

Unsupervised learning does not require labeled data. 🔜Instead, the model learns patterns and structures from the data itself without explicit guidance on what the output should be. Most LLMs are initially trained using unsupervised learning, where they learn to predict the next word in a sentence given the previous words. This process is based on a vast corpus of text data that is not labeled with specific tasks. For instance, instead of receiving both the question and 🧜answer like above in the supervised example, the model is only fed the question and must aggregate and predict the output based only on inputs.

In practice, many LLMs use a c🐟ombination of both unsupervised and supervised learning. The model might first undergo unsupervised pre-training on large text datasets to learn general language patte𝓰rns, followed by supervised fine-tuning on task-specific labeled data.

Advantages of Large Language Models

While technology can offer advantages, it can also have flaws—and large la⛎nguage models are no exception. As LLMs continue to evolve, new obstacles may be encountered while other wrinkles are smoothed out.

Her🔥e are some ofℱ the main advantages of large language models:

• Increased efficiency for users: Using large language models to generate content can save time for individuals and businesses that rely on text-based content. Instead of spending hours writing a single marketing email or blog post, you can use a tool like ChatGPT to create it in minutes.
• Wide variety of applications: Large language models are not limited to use in any one industry or field. Their adaptability and accessibility can make them suited to a number of uses across different fields.
• Ever-evolving technology: AI technology is changing all the time, and large language models are constantly being refined to increase their accuracy. Each new innovation represents a potential new opportunity to put LLMs to use and learn just how much they’re actually capable of doing.

Limitations of Large Language Models

The main limitation of large language models is that while useful, they’re not perfect. The quality of the content that an LLM generates depends largely on how well it’s trained and the information that it’s using to learn. If a large language model has key knowledge gaps in a specific area, then any answe🔴rs it provides to prompt🅘s may include errors or lack critical information.

Large language models primarily face challenges related to data risks, including the quality of the data that they use to learn. Biases are another potential challenge, as they can be present within the datasets that LLMs use to learn. When the dataset that’s used for training is biased, that can then result in a large language model generating and amplifying equally biased, inaccurate, or unfair responses.

Concerns of stereotypical reasoning in LLMs can be found in racial, gender, religious, or political bias. For instance, an MIT study showed that some large language understanding models scored between 40 and 80 on ideal context association (iCAT) texts. This test is designed to assess bias, where a low score signifies higher stereotypical bias. In comparison, an MIT model was designed to be fairer by creating a model that mitigated these harmful stereotypes through logic learning. When the MIT model was tested against the other LLMs, it was found to have an iCAT score of 90, illustrating a much lower bias.

A separate study, from Stanford University in 2023, shows the way in which different language models reflect general public opinion. Models trained exclusively on the internet were more likely to be biased toward conservative, lower-income, less educated perspectives. By contrast, newer language models that were typically curated through human feedback were more likely to be biased toward the viewpoints of those who were liberal-leaning, higher-income, and attained higher education.

The Bottom Line

Large language models (LLMs) are something the average person may not give m♔uch thought to, but that could change as they become more mainstream. For example, if you have a bank account, use a financial advisor to manage your money, or shop online, odds are you already have some experience with LLMs, though you may not realize it.

Learning more about what large language models are designed to do can make it easier to understand this new technology and how it may impact day-to-day life now🌌 and in the years to come.