What are large language models (LLM)? Definition & Examples

A large language model (LLM) is an AI language model that processes vast amounts of data and can understand, summarize and generate texts as well as carry out other tasks. Machine learning technology forms the basis of LLMs, which work with patterns that they identify in the datasets they are given.

What are the primary features of large language models?

Large language models (LLMs), also referred to as AI language models, are, in the broadest sense, neural networks. A defining feature of LLMs is their ability to help computers independently solve problems. Computers can also improve upon their capabilities with LLMs. Thanks to artificial intelligence and deep learning, LLMs can train themselves as long as they have enough data that is up to date.

Large language models can perform various tasks in natural language, including but not limited to:

• Summarizing information
• Translating information
• Supplying Information
• Creating texts
• Recognizing and predicting text patterns

What are large language models used for?

LLMs can be trained for a range of tasks and use cases. Generative AI is one of the most popular ways that LLMs are used. Using prompt engineering, generative AI models can generate new content or data based on the data they have been trained on. Below, we’ve summarized some of the most popular use cases for large language models:

• Text generation: LLMs are ideal for AI programs that generate texts. It doesn’t matter what the length of the text is or what type of text you need. They can be used for writing a poem, an email, a blog post, a news article or a production description.
• Text analysis and optimization: A well-trained large language model can help you check texts for errors and also make recommendations for improvements. Another typical use case is text translation.
• Programming: AI language models can also be an excellent tool for developers. They can, for example, check for errors in written code or automate the creation of recurring components.
• Sentiment analysis: With large language models, you can summarize and analyze the emotional tone of customer reviews, blog comments and social media reactions when conducting sentiment analysis.
• Chatbots: Chatbots that use LLMs are the perfect solution for providing users with quick answers to questions they may have about products and services.
• DNA research: When analyzing DNA sequences, AI tools that rely on a large language mode can significantly simplify analysis work. For example, they can help identify recurring or notable patterns in DNA strands.
• Processing audio and visual material: In daily work with images and sound, LLMs can also provide substantial support. They can be used to generate subtitles in different languages, recognize speech patterns and faces, and create new images or songs.

How do LLMs work?

Artificial intelligence cannot handle unstructured data (e.g., free text or images) on a fundamental level. Instead, it relies on numerical values. To work with natural language, LLMs are built using Transformer models. These models convert input prompts into tokens. Each token represents a part of a word (subword), which has been assigned a unique ID. This provides the large language model with a numerical value for each token, allowing it to grasp and interpret the individual elements of the prompts. To achieve optimal processing, sometimes several hundred billion parameters are used, with the parameters being optimized on a continuous basis.

The LLM establishes statistical connections between tokens, allowing it to recognize patterns, such as the context in which subwords most frequently occur, and how sentences in a paragraph relate to each other. During output, a large language model first generates tokens, which are then converted into natural language. The response is based on probabilities: tokens with lower probabilities are used less frequently that those with higher probabilities. By adjusting the LLM temperature parameter (the higher the value, the more creative the responses), one can also prompt a large language model to choose terms that are less common.

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What are some of the most notable LLMs?

Large language models play a significant role in today’s business world. When used effectively, they offer various benefits to companies, including improved customer retention, innovation, enhanced decision-making processes and, above all, increased productivity and efficiency. With so much to offer, the large number of AI language models available is not surprising. Below, we’ve summarized some of the most important solutions on the market:

• GPT-3.5 and GPT-4: Open AI’s GPT-3.5 and GPT-4 are among the most well-known large language models. The two members of the GPT family (Generative Pre-trained Transformer) form the foundation of the globally successful chatbot ChatGPT. Some sources have suggested that Version 4 likely operates with over 1 trillion parameters.
• BERT: BERT (Bidirectional Encoder Representations from Transformers) is a large language model developed by Google that has been used for various natural language processing applications ranging from search engines (including Google itself) to chatbots. 340 million different parameters are used in BERT-Large.
• PaLM: PaLM (Pathways Language Model) or PaLM 2 is Google’s LLM-based chatbot. With 540 billion parameters, ChatGPT‘s contender distinguishes itself with its sophisticated understanding of formal logic, mathematics and coding.
• LlaMA: The open-source large language model LlaMA (Large Language Model Meta AI) comes from the Facebook’s parent company Meta. With LlaMA, Meta aims to provide developers, researchers and companies with the opportunity to develop, test and responsibly scale generative AI ideas. Depending on the model you choose, the LLM employs 8 or 70 billion parameters.
• Claude: Claude is an LLM solution from Anthropic designed to provide results that are as helpful, harmless and accurate as possible. Anthropic’s goal is to create an AI solution that is more ethical and responsible than the alternatives that are currently available.