An Introduction to Large Language Models

from | 5 July 2024 | Basics

Large Language Models (LLMs for short) have brought generative AI to the forefront of business interest as they are used for various organisational functions and use cases. These AI systems generate human-like texts by learning from large amounts of data. Organisations are currently using LLMs for language translation, content creation and other applications. Large language models are constantly evolving, improving and changing the way organisations use technology, making them an unprecedented part of business efficiency and the modern digital landscape. This blog post will therefore explore what LLMs are, how they differ from natural language processing (NLP), what their working architecture looks like and how they are used in organisations. 

What are Large Language Models?

Large Language Models (LLMs for short) are a type of Type of Foundation Models (also known as basic models, FM for short), which are trained on large amounts of text data. This enables them to understand and output texts in natural language. These models are designed to understand and produce text like humans. The current advanced capabilities of LLMs include:

  • Draw conclusions from the context
  • Generation of contextually and coherently relevant answers
  • Translating text into languages other than English
  • Summarising text
  • Answering questions
  • Support with code generation

Large Language Models can fulfil such a wide variety of text tasks due to the billions of parameters that facilitate the capture of complicated language patterns. However, since large language models are very large and require extensive resources Small language models (SLMs) are becoming increasingly popular in business applicationsas they require fewer parameters. SLMs require less computing power, are accessible to a wide range of researchers and can be easily customised for business applications. Despite their advantages, the choice of SLMs over LLMs is challenging due to the limited knowledge of SLMs and their limited understanding of language and context. Nonetheless, their discovery is a significant step towards democratising the artificial intelligence (AI)as it is made freely accessible. 

Introduction to foundation models, numerous data in an abstract space

Find out all about foundation models and how they can be used effectively in companies to give you a competitive edge and accelerate business processes in our basic article.

An Introduction to Foundation Models

Large Language Models vs. Natural Language Processing 

Large Language Models (LLMs) represent a significant breakthrough in the Natural Language Processing (NLP) represent. NLP is a broad field, which is based on the Interaction between computers and languages is focussed. It is about the ability of a computer to interpret, understand and generate human language. This process enables the understanding and generation of texts, the translation of languages and speech recognition. LLMs, a subgroup of NLP and specific classes of models with NLP capabilities, enable similar functions and are also used to improve NLP results.

FeatureLarge Language ModelNatural Language Processing
Centre of gravityText creation   Language analysis
Skillslimited language comprehension skills, as they focus primarily on text productionHigh level of language comprehension due to his ability to analyse language
DifferencesAdaptable, as the models can solve different language tasks without having to be trained for each taskThe new technology uses algorithms to regenerate human language, closing the gap between digital systems and human communication
TechnologiesDeep learning, transformer architecture, mechanisms for self-observation and scalabilityVarious processes, such as parsing, sentiment analysis, speech recognition and machine translation
ApplicationsContent creation, providing automated responses through chatbots and facilitating communication through language translationwide-ranging applications, such as analysing text to extract meaningful insights, adapting content suggestions based on user preferences, etc.
ChallengesDifficulties with language comprehension, leading to inappropriate responses in complex situations, bias in the training dataAmbiguity of human language, distortions ("bias") in the data used, high computing power
Differences between Large Language Models and Natural Language Processing
Natural Language Processing

The natural, spoken language of humans is the most direct and easiest way to communicate. Learn how machines and algorithms use NLP in innovative ways:

Natural Language Processing (NLP): Natural language for machines

Architecture of a Large Language Model

Large Language Models (LLMs) work with Deep learning techniques and large amounts of text data. They are based on the Transformer architecture, such as the generative pre-trained Transformer. The models are characterised by their ability to process sequential data, such as text input. LLMs consist of several layers of neural networks whose parameters can be fine-tuned during training. The attention mechanism, which focuses on specific parts of data sets, further enhances this process. To facilitate understanding, let us first understand the core components of the LLM, followed by its training process and its relationship to the generative AI

A Large Language Model has the following Main componentsnamely

  • Encoder-decoder setupThe encoder processes the input text and the decoder generates the output text. Both the encoder and the decoder consist of several layers. 
  • Attention mechanismThe mechanism allows the model to focus on the input segments that are most relevant for generating each part of the output.

The Training of a large language model requires the following superordinate steps:

  1. Identification of the targetThe LLM training process starts with a specific use case for the model, as the target determines the data sources for training the model. The target and the LLM use case are constantly evolving to incorporate new elements during training and fine-tuning.
  2. Pre-trainingAfter identifying the use case, it is necessary to collect and cleanse the data in order to standardise it. 
  3. TokenisationOnce the standardised data set is ready, it is important to break down the text within the data set into smaller units. This makes it easier for the LLM to understand words and sub-words. The process of tokenisation enables the LLM to understand sentences, paragraphs and documents as it first learns words and sub-words. This process enables the activation of the transformer model and the neural networkwhich belong to a category of AI models that are able to understand the context of sequential data.
  4. Selection of infrastructureThe next step is to provide suitable computing resources, e.g. a high-performance computer or a Cloud-based Servers.  
  5. TrainingOnce the computing resources are available, it is time to set the parameters for the training process, e.g. the stack size or the learning rate.
  6. Fine-TuningOnce the model receives data for training, its results are evaluated and the parameters are further adjusted to improve the results of the LLM. This process is called fine-tuning and helps to adapt the model to a specific task. 

All large language models fall under the category of generative AI. Generative AI covers a Wide range of AI models that create new content such as text, images, videos and more. Both large language models and generative AI can utilise a transformer architecture. Transformers efficiently capture contextual information and wide-ranging dependencies, making them particularly useful for various language tasks. Transformers can also be used to generate images and other types of content. 

Large Multimodal Models, a house wall consisting of numerous structures, such as coloured rectangles and honeycomb-shaped alignments, with a person walking past in front of it

Large Multimodal Models close the gap of conventional language models by working with different data modalities such as images, sound and text and have the potential to improve business processes.

An Introduction to Large Multimodal Models

Examples of relevant LLMs 

The LLM landscape is full of options, so in this section we will explore some of the most popular Large Language Models and highlight their key benefits for organisations. 

LLMManufacturerDescription
GPT-4OpenAIA powerful LLM known for its text creation skills.
GeminiGoogleA lightweight model that is ideal for fast and cost-effective tasks such as data extraction or captioning.
PALMGoogleExcellent for logical thinking, logic and complex coding tasks.
CLAUDEAnthropicDeveloped as a helpful AI assistant that excels at summarising and analysing texts.
FalconTechnology Innovation Institute (TII)An open source model with strengths in text creation, translation and answering questions.
VICUNA 33BLMSYSA powerful LLM designed for chatbot research and promising in NLP research and chatbot development.
MPT-30BMosaic MLHandles large data sets effectively and performs well in sentiment analysis and big data processing for financial and scientific applications.
Examples of large language models
Top 14 LLMs in Business, a cubist collage of language

Large language models are transforming interaction with technology and expanding its application from content creation to customer service. Our overview presents 14 relevant representatives in detail:

The 14 Top Large Language Models: A Comprehensive Guide

Use cases for LLMs in companies

Large Language Models (LLMs) are taking over organisations' workflows by transforming various aspects of business. In this section, we explore the role of LLMs in redefining business processes and the exciting opportunities and challenges they bring:  

Automation of customer support

DescriptionLarge Language Models offer companies the opportunity to automate customer support processes in companies. Large language models can analyse customer enquiries, provide precise answers or forward them to suitable human employees. 

PotentialsUsing LLMs to automate customer support can streamline customer service processes, shorten response times and improve overall customer satisfaction. 

ChallengesIt is a challenge to ensure that language models understand the context correctly. LLMs are currently unable to handle complex requests effectively. Integrating large language models into the existing customer support infrastructure requires additional resources to ensure consistency and quality of service.

Content creation for social media and marketing

DescriptionLarge Language Models create various types of content, such as promotional articles, product descriptions and marketing texts for companies from different industries.

PotentialsLLMs can help companies to scale their content production and tailor content more efficiently to specific target groups. 

ChallengesIt's a painstaking process to ensure that the content generated is consistent with brand language and messaging guidelines and maintains originality while avoiding plagiarism. Marketing teams and AI specialists need to allocate dedicated time and resources to integrate LLMs into content creation workflows. 

Analysing data and gaining insights

DescriptionCompanies use large language models to analyse large amounts of unstructured data, e.g. customer preferences, feedback, market trends and conversations on social media. 

PotentialsLLMs can support business decisions by gaining valuable insights, recognising patterns and making predictions. 

ChallengesAnalysing data using large language models can lead to company data being subject to data protection. Another challenge is ensuring the accuracy and reliability of the insights gained from LLMs and integrating these insights into existing analytics platforms. Interpreting and implementing the results of LLMs requires investment in human oversight to avoid biased or misleading conclusions, which could drive up costs for the organisation. 

Support in complying with legal regulations

DescriptionLarge Language Models can help companies navigate complex legal frameworks by providing relevant information, drafting documents and analysing contracts. 

PotentialsThis can help to streamline legal processes, reduce costs and minimise compliance risks. 

ChallengesEnsuring the legal information accessed by LLMs is accurate and up to date, and addressing potential ethical concerns such as client confidentiality, is a painstaking process. Integrating LLMs into legal workflows also requires close collaboration between legal teams and AI experts to ensure that the technology effectively complements human expertise.

With large language models for Industry 5.0

Large-scale language models have revolutionised the business world due to their revenue-boosting and cost-cutting applications. The technology has only been publicly and commercially available for a few years, but we have yet to see how it will evolve in the coming years as its challenges such as data bias and huge computing power requirements remain. Nevertheless, the future for Large Language Models promises greater integration and impact across all industries.

Author

Patrick

Pat has been responsible for Web Analysis & Web Publishing at Alexander Thamm GmbH since the end of 2021 and oversees a large part of our online presence. In doing so, he beats his way through every Google or Wordpress update and is happy to give the team tips on how to make your articles or own websites even more comprehensible for the reader as well as the search engines.

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