Large Language Models (LLMs) 101: Definitions, History, Key Concepts and Use Cases

    By Joan Nugent, Content Executive, BJSS

    In this blog, we'll delve into the world of Large Language Models (LLMs), including defining what they are, their evolution, early limitations, and subsequent milestones. We'll explore how LLMs work and their applications in text classification, chatbots, and more. To conclude we will investigate the future of LLMs, with insights and predictions from industry stakeholders.

    What is a large language model?

    An LLM is an artificial intelligence (AI) algorithm that can mimic human intelligence, using deep learning techniques and large data sets to understand, summarise, generate and predict new content.

    LLMs are a type of generative AI that have been specifically architected to help generate text-based content by analysing vast amounts of data, learning the patterns and connections between words and phrases.

    What is meant by generative AI?

    Generative AI refers to AI techniques that learn a representation of artifacts from data, and use it to generate brand-new, unique artifacts that resemble but don’t repeat the original data. Generative AI can produce totally novel content (including text, images, video, audio, structures), computer code, synthetic data, workflows and models of physical objects.

    With generative AI gaining more and more interest, the majority of AI systems are powered by LLMs that dominate the market. LLMs are used for a variety of tasks, for example, chatbots, search engines, creative writing, summarisation, and education.

    There are several LLMs available, some of them open source, e.g., LlaMA. Perhaps most well-known is OpenAI’s Generative Pretrained Transformer (GPT), which powers the AI chatbot ChatGPT.

    The significance of LLMs in the field of natural language processing (NLP)

    An LLM is a machine learning model that can perform a variety of natural language processing (NLP) tasks such as generating and classifying text, translating text into other languages, and answering questions. The label “large” refers to the number of values (parameters) the language model can change autonomously as it learns. Some of the most successful LLMs have hundreds of billions of parameters.

    LLMs work by analysing massive datasets using neural networks with billions of adjustable parameters. During training, the parameters are optimised to capture intricate linguistic patterns and relationships. Leading LLMs today have between 10 billion to over 1 trillion parameters. For example, GPT-3 has 175 billion, while Google's Switch Transformer reaches 1.6 trillion parameters. There is no hard limit and model sizes will likely keep increasing.

    LLMs can generate coherent, context-aware text responses, leveraging unsupervised learning, enabling them to handle various NLP tasks without extensive fine-tuning or explicit supervision.

    LLMs have transformed the field of NLP by providing models with a deeper understanding of language and the ability to generate human-like text. The advancements in LLMs have been instrumental in pushing the boundaries of what NLP can achieve.

    How Do LLms Work?

    A Large Language Model (LLM) is a neural network, a machine learning model composed of mathematical units called neurons. These neurons compute outputs based on inputs, and their power lies in their connections to one another, each with a specific weight. While a basic neural network can be small with a few neurons and connections, LLMs are immense, boasting millions of neurons and hundreds of billions of connections, enabling complex language understanding and generation. The specific neural network type of LLMs employs the transformer architecture for sequential data processing, such as text.

    Neural network architectures define how neurons are interconnected in layers. Transformers, introduced by Google in 2017, utilise the concept of "attention," where some neurons have stronger connections to others in a sequence. This architecture suits text processing, as text unfolds sequentially, with interdependencies, making the strength of connections critical for understanding context and meaning.

    A machine learning or AI model is essentially a computer program that processes input data and produces an output. What sets these models apart is that, instead of explicitly programming the instructions, human programmers create algorithms that use extensive existing data to construct the model. In the case of Large Language Models (LLMs), programmers define the model's architecture and construction rules but not the individual neurons or their connections. The model shapes these elements during a training process, where it analyses vast amounts of text data. Initially, the model's output is nonsensical, but is improved through continuous refinement and comparison to its input, eventually generating human-like text. With sufficient resources and data, LLMs can produce text nearly indistinguishable from human-written content.

    A Large Language Model (LLM) predicts the next word in a sequence of text by leveraging its complex neural network architecture, typically based on the transformer model. This predictive ability stems from its capacity to learn intricate patterns and relationships within vast text corpora during the training phase. When confronted with a sentence, the LLM utilises its contextual understanding and the inherent connections between its neurons to estimate the probability of each possible word following the preceding ones. It evaluates factors like context, semantics, grammar, and prior words to generate highly accurate predictions.

     

     

    LLms and foundation models

    Foundation models are large, powerful AI systems that can be used to solve a wide range of problems. They are trained on massive datasets of text, code, and other types of data, giving them the ability to learn patterns and relationships that would be difficult or impossible for humans to identify.

    LLMs are a type of foundation model that are trained on text data, which gives the ability to understand and generate human language in a variety of ways.

    Prompt engineering for foundation models

    Prompt engineering involves strategically crafting the prompts fed into large language models to shape their outputs. It is an essential technique for controlling foundation models' behaviour and responses. Effective prompt engineering requires choosing relevant keywords, supplying contextual details, and specifying the desired output format. This careful prompt formulation allows users to actively steer these powerful models towards generating targeted, high-quality results.

    Zero-shot learning

    Models intended for zero-shot learning can theoretically perform all kinds of tasks as long as they receive appropriate prompts. Zero-shot learning involves training a model to generalise and make predictions on unseen tasks. To perform prompt engineering in zero-shot learning environments, prompts should be constructed that explicitly provide information about the target task and the desired output format.

    Applications of Large Language Models

    Natural language understanding tasks

    Large Language Models (LLMs) have found extensive applications in natural language understanding tasks, revolutionising various fields. Text classification benefits from LLMs' ability to categorise documents, emails, and news articles with remarkable accuracy. Sentiment analysis leverages LLMs to gauge public opinion, sentiment, and emotional tone in social media, reviews, and customer feedback. LLMs also work to enhance named entity recognition, identifying entities like people, places, and organisations in text.

    Natural language generation tasks

    Large Language Models (LLMs) have ushered in a new era of natural language generation, transforming various fields. Text generation is one prominent application, enabling automated content creation for news articles, reports, and even creative writing. Chatbots powered by LLMs offer interactive and personalised customer support. Language translation benefits from LLMs' ability to generate contextually accurate translations. Additionally, LLMs are crucial for content summarisation, extracting key information from lengthy texts.

    The specific business uses and benefits of llms

    Large Language Models (LLMs) offer diverse business applications and benefits. They enhance customer service through chatbots, providing 24/7 support and improving user engagement. LLMs enable automated content generation, reducing content production costs. In data analysis, they aid in extracting insights from unstructured text data, accelerating decision making. LLMs also enhance language translation, making global markets more accessible. Moreover, they assist in sentiment analysis for brand management. Overall, LLMs streamline operations, enhance customer experiences, and drive innovation, making them invaluable assets for businesses seeking efficiency and competitiveness in the digital age.

    Challenges and limitations of llms

    Large Language Models face several challenges at different stages of their use:

    • Training: The initial challenge lies in training LLMs. It requires massive computational resources.
    • Data Bias: LLMs can learn biases present in training data, leading to biased or unfair outputs when used. This bias can occur during the training stage and persist in later usage.
    • Fine-tuning: Fine-tuning LLMs on specific tasks can be challenging due to the need for large, high-quality datasets, and the risk of further amplifying biases.
    • Interpretability: Understanding how LLMs arrive at their answers remains a challenge. Lack of transparency in their decision-making process can lead to mistrust and ethical concerns.
    • Safety and ethical use: Ensuring LLMs are used responsibly and do not produce harmful or malicious content is a significant challenge.
    • Scalability: Scaling LLMs to handle diverse tasks and languages without deteriorating performance and quality is a challenge as it can introduce errors and reduce reliability.
    • Resource consumption: Running LLMs at scale requires substantial computing power.
    • Deployment: Integrating LLMs into real-world applications while addressing these challenges poses difficulties in ensuring their effective and ethical use.
    How LLM limitations can be mitigated

    Mitigating the limitations of Large Language Models (LLMs) involves a combination of technical, ethical, and operational strategies. This includes implementing bias detection and correction algorithms to reduce bias during training and fine-tuning. Using diverse and representative datasets helps address bias issues. Involving human reviewers is crucial for providing guidance and identifying biases during model development. Additionally, developing methods for model interpretability, like attention maps or decision rationales, contributes to making LLM outputs more understandable. Ensuring ethical use and content moderation involves establishing clear guidelines and principles for LLM usage, investing in content moderation systems, and engaging with users and communities to refine content filtering. Finally, fostering transparency, continuous improvement, and responsible deployment practices are essential elements in mitigating LLM limitations.

     

     

    LLm Hallucinations

    In the context of Large Language Models (LLMs), "hallucinations" refer to instances where the model generates outputs or responses that are factually incorrect or imaginary. These outputs are not based on any real-world information or data but are instead the result of the model's overgeneralisation or creative extrapolation of its training data. Hallucinations occur when LLMs generate text that sounds plausible but is fundamentally untrue.

    For example, if a medical LLM were to hallucinate, it might provide incorrect medical advice or information that could potentially harm users. Similarly, a news-based LLM might generate fictional news stories or events that never occurred.

    Hallucinations are a significant concern in the deployment of LLMs, especially in applications where accuracy and reliability are critical. Addressing hallucinations often involves fine-tuning and bias mitigation, as well as careful monitoring and content moderation.

    Conclusion

    The significance of LLMs

    Mitigating the limitations of Large Language Models (LLMs) involves a combination of technical, ethical, and operational strategies. This includes implementing bias detection and correction algorithms to reduce bias during training and fine-tuning. Using diverse and representative datasets helps address bias issues. Involving human reviewers is crucial for providing guidance and identifying biases during model development. Additionally, developing methods for model interpretability, like attention maps or decision rationales, contributes to making LLM outputs more understandable. Ensuring ethical use and content moderation involves establishing clear guidelines and principles for LLM usage, investing in content moderation systems, and engaging with users and communities to refine content filtering. Finally, fostering transparency, continuous improvement, and responsible deployment practices are essential elements in mitigating LLM limitations.

    Large language models (LLMs) are transformative in contemporary technology. Their significance lies in their ability to understand and generate human language at an unprecedented scale. LLMs power chatbots, virtual assistants, and language translation, facilitating global communication. They streamline content creation, from articles to code, enhancing productivity. Moreover, they democratise access to information, aiding knowledge dissemination and learning.

    Responsible development and deployment

    Responsible development and deployment of LLMs is crucial to ensure ethical AI governance. LLMs can perpetuate biases, spread misinformation, and harm privacy if not managed carefully. Proper governance mitigates these risks, fostering technology that benefits society while upholding ethical standards, trust, and accountability in AI-driven applications. In this blog post BJSS explores how organisations can strike the balance between driving innovation and responsibly implementing AI.