Large language models are revolutionizing artificial intelligence by enabling advanced text generation and comprehension. Using deep learning techniques and extensive text datasets, these models perform a variety of natural language processing tasks. This introduction covers the basics of LLMs, their functioning, key components, and business applications.
What are large language models (LLMs)?
In a nutshell, an LLM is a sophisticated type of artificial intelligence (AI) designed to understand, generate, and manipulate human language. These models utilize deep learning techniques, in particular transformer neural networks, and are trained on extensive text data.
Technically speaking, LLMs excel in a variety of natural language processing tasks, such as generating text, translating languages, summarizing content, conducting sentiment analysis, and answering questions. At the core of these models is the transformer architecture, which incorporates self-attention mechanisms. This enables them to process and produce language effectively based on contextual cues from the input data.
Large language models are also known as neural networks (NNs), which are computing systems inspired by the human brain. These networks function through a layered arrangement of nodes, similar to neurons, that work together to process information.
Another important aspect in the context of LLMs is their parameters. These are the elements of the model that are learned from the training data. Essentially, they are the internal settings of a neural network that are adjusted during training to reduce the discrepancy between the model’s predictions and the actual outcomes, such as the next word in a sentence.
For a model to be classified as “large,” it typically contains billions of parameters. The sheer number of parameters directly influences an LLM’s ability to understand and generate human-like text. Generally, more parameters result in a better memory for detail, which leads to more accurate and contextually appropriate outputs. Thus, having a large number of parameters equips LLMs to perform complex tasks with high proficiency.
How do large language models work?
The core technology behind LLMs is the transformer model, which was introduced in the seminal paper “Attention is All You Need” by Vaswani et al. in 2017. The transformer architecture consists of an encoder and a decoder, both of which use self-attention mechanisms to process and generate text.
The first step in processing text with AI models is tokenization, which involves converting text into a series of tokens. In natural language processing (NLP), a token is a fundamental unit of data, such as a word, character, or subword. Tokenization breaks text down into these units. These tokens are typically transformed into numerical embeddings that can be processed by the model.
Embeddings are dense vector representations of tokens that capture semantic, syntactic, and contextual meanings. To further enhance the transformer model’s ability to process sequences of text, positional encodings are added to these embeddings. Positional encodings use a mathematical function to generate a unique vector for each position in the sequence, which is then added to the token embeddings. This process embeds information about the order of tokens, enabling the model to consider the arrangement of words in the sequence alongside their contextual meanings.
After tokenization, embedding, and the addition of positional encoding, each token’s enriched vector representation is processed by the encoder. In the case of transformer models, the encoder utilizes self-attention mechanisms that allow the model to analyze and weigh the importance of all other tokens in the sequence for each token. This capability enables the model to effectively understand the context and relationships between words in a sequence, even if they are far apart.
The decoder then takes the encoded representations and generates the output text. It uses the context provided by the encoder to produce text that is coherent and contextually appropriate.
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Components of large language models
Let’s break LLMs down into specific components.
LLMs consist of multiple neural network layers that work together to process input text and generate output content. These include recurrent layers, feedforward layers, embedding layers, and attention layers.
The embedding layer converts the input text into embeddings, capturing both the semantic and syntactic meaning. This allows the model to understand the context of the input.
The feedforward layer (FFN) consists of multiple fully connected layers that transform these embeddings. This transformation helps the model extract higher-level abstractions and understand the user’s intent behind the text input.
The recurrent layer processes the words in the input text sequentially, capturing the relationships between words within a sentence.
Finally, the attention mechanism allows the model to focus on specific parts of the input text that are most relevant to the task at hand, enabling it to produce the most accurate outputs.
Generative AI vs. LLMs
You may wonder what LLMs have to do with generative AI. Let’s explain it in more details.
Generative AI is an umbrella term that encompasses a range of artificial intelligence models designed to create content, including text, code, images, videos, and music. The most popular examples of generative AI tools are Midjourney, DALL-E, and ChatGPT.
Within this category, LLMs represent a specific type of generative AI that focuses on text. These models are trained on extensive text datasets to produce textual content. GPT-3 is a well-known example of this type of generative text AI. GPT-4, on the other hand is already a multimodal model capable of accepting images as well as text as input.
Essentially, all LLMs are a subcategory of generative AI.
Training of large language models
Training an LLM involves a three-step process: pretraining, fine-tuning, and alignment. The model can also be steered toward the desired outcome through prompt engineering.
Pretraining
During this initial phase, LLMs are trained on vast amounts of raw text data primarily sourced from the Internet. This stage utilizes unsupervised learning techniques, which do not require human intervention, to label the data. The objective is to learn the statistical patterns of language.
To improve the accuracy of LLMs, the prevailing strategy is to enlarge the model—achieved by increasing the number of parameters—and to expand the training data. Consequently, the most advanced LLMs, such as PaLM 2 with 540 billion parameters and GPT-4 estimated at around 1.8 trillion parameters, are trained on extensive data sets. This approach, however, poses accessibility challenges due to the sheer size of the models and the scale of data required, making the pretraining process time-consuming and costly, affordable only to a limited number of well-resourced companies (see Figure 1).
Fig 1. Parameter counts for various language models
Fine-tuning
While pretraining equips a large language model (LLM) with a basic understanding of language, alone it is insufficient for performing specialized tasks with the desired accuracy. To refine a model, It is put through a process known as fine-tuning. This involves adjusting a model’s parameters using new datasets that mirror the specific tasks it will perform. The process includes training on examples that directly align with the desired outcomes, such as detecting sentiment in text or composing emails.
In the fine-tuning process for LLMs, data quality is crucial. The dataset must be large, often requiring thousands of examples, and be meticulously clean and well-formatted to prevent errors. It is also essential that the data is representative of the real-world scenarios in which the model will operate, such as including a diverse range of sources and emotions for sentiment analysis. Additionally, the dataset should be detailed enough to guide the model effectively, providing specific inputs to achieve the desired outputs, such as the correct tone and style for email generation. A well-curated dataset ensures the model performs accurately and reliably across tasks.
Alignment
After fine-tuning, alignment in LLM training ensures that the model’s outputs are in line with ethical standards and specific human values. This phase involves strategies like Reinforcement Learning from Human Feedback (RLHF), where human evaluators guide the model by providing positive reinforcement. Techniques such as Direct Policy Optimization (DPO) and Knowledge Transfer Optimization (KTO) are used to establish strict guidelines, ensuring ethical compliance.
Additionally, self-play allows the model to refine its decision-making by engaging in simulated dialogues with itself, enhancing its capability to handle complex scenarios autonomously. This rigorous alignment process is crucial for applications in sensitive areas like healthcare, customer service, and legal advice, ensuring the model operates within desired ethical and practical boundaries.
Prompt engineering
Prompt engineering, also known as in-context learning, involves crafting input prompts that include specific instructions or examples to guide an LLM’s output without the need for traditional retraining. The model adapts to new tasks based solely on the input prompts, without altering its internal parameters. Prompt engineering is not the next stage of LLM training, but rather a useful technique allowing users to achieve desired behavior without the costs and complexities of technical fine-tuning. Furthermore, well-designed prompts help prevent catastrophic forgetting by enabling the model to effectively apply previously learned information to new situations, thereby maintaining robust performance even as it shifts between tasks.
The stages of the LLM training process are presented in Figure 2 below:
Fig. 2 LLM training stages (source)
Types of large language models
LLMs can be categorized based on their architecture, training methodologies, and specific applications. Here are the primary types of LLMs:
Autoregressive language models
Autoregressive models generate text by predicting the next word in a sequence based on the preceding words. They are trained to maximize the likelihood of the next word given the context. These models excel at generating coherent and contextually relevant text but can be computationally expensive and may produce repetitive or irrelevant responses.
Example: GPT-3 (Generative Pre-trained Transformer 3) by OpenAI, which has 175 billion parameters and is known for its ability to generate human-like text without the need for task-specific fine-tuning.
Transformer-based models
Transformers are a type of deep learning architecture that process and generate text by capturing long-range dependencies and contextual information. These models use self-attention mechanisms to focus on different parts of an input sequence, making them highly effective for various NLP tasks.
Example: BERT (Bidirectional Encoder Representations from Transformers) by Google, which uses bidirectional training to understand the context of words based on their surroundings.
Encoder-decoder models
Encoder-decoder models are commonly used for tasks like machine translation, summarization, and question answering. They consist of an encoder that processes the input sequence and a decoder that generates the output sequence. The encoder learns to encode the input information into a fixed-length representation, which the decoder uses to generate the output.
Example: MarianMT (Marian Neural Machine Translation), which is mainly being developed by the Microsoft Translator team with contributions from universities (most notably the University of Edinburgh and, previously, the Adam Mickiewicz University in Poznań) and commercial users.
Pretrained and fine-tuned models
These models are pretrained on large datasets and then fine-tuned on specific tasks. Pretraining involves learning general language patterns, while fine-tuning adapts the model to perform specific tasks more effectively.
Example: T5 (Text-to-Text Transfer Transformer) by Google, which is trained using a text-to-text framework and can perform a wide range of language tasks by transforming input and output formats into text.
Multilingual models
Multilingual models are trained on data from multiple languages, enabling them to perform tasks across different languages. These models are particularly useful for applications requiring cross-lingual understanding and translation.
Example: GPT-4, which can answer questions in multiple languages and shows high accuracy in both English and other languages, such as Telugu.
Instruction-focused models
Models designed to follow instructions are specifically trained to generate responses based on explicit prompts in the input. These models perform exceptionally well in scenarios that demand adherence to detailed instructions, such as in sentiment analysis, text generation, and programming tasks.
Example: Instruction-tuned versions of GPT-3, such as text-davinci-003, which are fine-tuned for following instructions and conversational interactions.
Conversation-focused models
These models are designed to predict the next response in a conversation, making them suitable for interactive communication applications like chatbots and conversational AI systems.
Example: ChatGPT, which is fine-tuned for conversational interaction with a human user.
Zero-shot models
Zero-shot models are generalized models trained on a broad corpus of data that can perform tasks without additional task-specific training. They can handle a wide range of tasks by leveraging their extensive pretraining.
Example: GPT-3 is often considered a zero-shot model due to its ability to perform various tasks without additional fine-tuning.
Multimodal models
Multimodal models can process and generate multiple types of data, such as text and images. These models are designed to handle more complex tasks that involve different data modalities.
Example: GPT-4o, also known as GPT-4 Omni, an advanced version of the GPT-4 model developed by OpenAI and released recently. This is a multimodal model, meaning it can process and generate outputs across text, image, and audio modalities.
What large language models can do
LLMs have lots of capabilities allowing them to perform various tasks, including:
- Text generation—LLMs can generate coherent and contextually relevant text based on a given prompt. This includes writing articles, creating stories, generating product descriptions, and more.
- Code generation—LLMs recognize coding patterns and generate code, proving indispensable in software development environments.
- Text summarization—LLMs can condense long documents, articles, and reports into shorter summaries, making it easier to digest complex information quickly.
- Language translation—LLMs can translate text from one language to another, supporting multilingual communication and expanding global reach.
- Question answering—LLMs can provide answers to questions based on the context provided, making them useful for customer support, educational tools, and information retrieval.
- Conversational AI—LLMs enable chatbots to understand user queries and respond in a conversational, natural manner, enhancing user interactions.
- Information retrieval—LLMs can extract and present information conversationally, similar to Bing AI, which integrates this capability into its search engine functionalities.
- Sentiment analysis—LLMs can analyze text to determine the sentiment expressed, such as positive, negative, or neutral. This is useful for understanding customer feedback, social media posts, and reviews.
- Text classification—LLMs can categorize text into predefined categories, such as spam detection, topic classification, and sentiment categorization.
Large language models business use cases
Based on their extensive capabilities, companies can leverage LLMs to address various business challenges. A prime example is customer service chatbots. These bots can significantly streamline customer service for both external and internal users by understanding questions—including ambiguous ones—and responding in natural language. Imagine a chatbot that handles a large volume of customer inquiries or an AI-powered service desk that assists users with common issues like password resets or simple troubleshooting, reducing the burden on the IT support team.
Sentiment analysis is another capability that can enhance customer service. Consider a scenario in a retail company that sells clothing online: customers can contact the service team via email or phone. All phone calls are automatically transcribed by AI and stored in a database along with email tickets. This database can then be analyzed to gauge customer sentiment toward the company and its products. This provides an invaluable source of insights, enabling companies to improve their service.
Knowledge management is yet another business application. When combined with a retrieval-augmented generation (RAG) mechanism that curbs hallucinations, chatbots powered by LLMs can search a company’s knowledge base for information. This allows employees to receive instant, verified responses to their queries, eliminating the need to tediously search through various databases and wikis.
The text generation and summarization capabilities of LLMs can streamline and enhance business content creation and management across various platforms. In marketing, LLMs efficiently produce SEO-friendly blog posts and craft engaging social media content. E-commerce businesses benefit from automated, unique product descriptions and responsive customer review management. For corporate communications, LLMs aid in drafting internal reports and ensure consistency in messaging while also providing editing and proofreading support.
A good example of practical business challenges solved by LLMs is the case of CarMax, a US-based car retailer. CarMax has utilized the Azure OpenAI Service to enhance its online customer experience by generating concise, informative summaries for thousands of car models, which has significantly improved the efficiency of content production and customer engagement on its website. This innovation allows customers to easily access key insights from reviews, helping them make informed decisions faster.
Of course, these are only examples of potential business use cases. There are many more possibilities that LLMs open to businesses. In fact, generative AI is slowly becoming ubiquitous in business, and it is hard to imagine doing business without it.
What are the challenges and limitations of LLMs?
It’s not always plain sailing though. LLMs present several limitations and challenges that need addressing to fully harness their potential in business applications.
First, there is the issue of hallucinations, where models generate plausible but incorrect or nonsensical information that can mislead users or distort facts. This is particularly problematic in domains like legal and healthcare, where accuracy is critical. To mitigate this, mechanisms such as fine-tuning and RAG are employed to curb these inaccuracies and enhance the reliability of AI-powered solutions.
Second, security and data confidentiality concerns arise when using publicly available LLMs, such as GPT-3, for business purposes. There is always the risk that sensitive company data used to fine-tune the model could become accessible to other users. A viable solution is to utilize Azure infrastructure, which allows companies to store their fine-tuned models in a dedicated environment equipped with enterprise-grade security measures.
Third, LLMs can perpetuate or even exacerbate biases present in their training data, leading to unfair outcomes or discrimination. This requires continuous monitoring and updating of the training datasets to ensure fairness and reduce bias, alongside employing techniques such as de-biasing during model training.
Additionally, LLMs operate as “black boxes,” offering little transparency in decision-making processes. This lack of transparency complicates efforts to debug or understand model behavior, posing challenges in sectors that require explainability, such as finance and regulatory compliance. Explainable artificial intelligence (XAI) could be a response to this challenge.
Finally, the training of these models is resource intensive, requiring significant computational power and energy, which not only increases costs but also impacts the environment. This makes LLM training feasible primarily for large companies with considerable resources. An alternative solution involves using small language models (SLMs), which are compact versions designed to operate efficiently with fewer resources. These SLMs can be fine-tuned for specific domains or tasks, achieving better performance and understanding within those particular areas without the hefty cost associated with LLMs.
What’s next—the future development of large language models
One of the most promising advancements in LLMs is their ability to generate their own training data. This capability addresses the challenge of data scarcity by allowing models to synthesize new content based on the knowledge they have acquired from diverse external sources. This self-training approach continuously improves the models, enhancing their performance and expanding their potential applications across various domains.
Future LLMs are also expected to incorporate self-fact-checking mechanisms to ensure the accuracy and reliability of the information they generate. By leveraging external sources to verify their outputs in real time, these models can provide references and citations to support their assertions. This feature enhances the trustworthiness of AI-generated content and mitigates the spread of misinformation, making these models more reliable for decision-making processes in critical fields like healthcare, finance, and law.
A novel architectural approach known as massive sparse expert models (MSEMs) is gaining traction. These models activate only the most relevant subset of parameters for a given input, significantly reducing computational overhead while preserving model performance. This approach enhances efficiency and scalability, making LLMs more practical for resource-constrained environments and applications requiring real-time inference.
Last but not least are multimodal capabilities. The integration of LLMs with other modalities, such as images, audio, and video, will open up new possibilities for comprehensive content understanding, generation, and summarization. This multimodal approach will enhance the versatility and applicability of LLMs in various contexts, enabling more intuitive and effective human-AI interactions.
One thing is sure: the rapid development of LLMs shows no signs of slowing down. LLMs will definitely shape our world in the near future and become critical in many business activities.
