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, summarise 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:

• Sum­mar­ising in­form­a­tion
• Trans­lat­ing in­form­a­tion
• Supplying In­form­a­tion
• Creating texts
• Re­cog­nising 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 sum­mar­ised 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 to write a poem, an email, a blog post, a news article or a pro­duc­tion de­scrip­tion.
• Text analysis and op­tim­isa­tion: 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 summarise and analyse 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 analysing 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, recognise 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 optimised on a con­tinu­ous basis.

The LLM es­tab­lishes stat­ist­ic­al con­nec­tions between tokens, allowing it to recognise 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.

To display this video, third-party cookies are required. You can access and change your cookie settings here.

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 sum­mar­ised 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.