What are Large Language Models (LLMs)?

If you’ve ever used a chatbot to resolve an issue, experimented with AI-driven writing assistants, or relied on automated translations during travel, you’ve likely already interacted with large language models (LLMs)—powerful systems that handle language tasks with striking fluency. These tools have integrated into our daily lives, shaping how we work, communicate, and create. From improving decision-making through the analysis of complex datasets to driving advancements in healthcare, LLMs are redesigning how businesses operate.

DigitalOcean’s 2023 Currents research report found that 73% of people use AI for business and personal work. The use cases are plentiful: optimizing supply chain management, identifying patterns in financial markets, and powering tools that generate strategic business insights. Whether you’re planning to integrate AI into your operations or evaluate AI business tools, this article will help you understand how LLMs work and their capabilities and limitations.

What is a large language model (LLM)?

An LLM is a language model designed to understand, generate, and process natural language. Built using deep learning architectures and transformer models, LLMs are pre-trained on vast amounts of text data to predict the next word in a sequence or solve other language-related tasks. These models use neural networks with billions of parameters, which helps them learn complex patterns, structures, and semantic relationships in human language.

How are LLMs trained?

LLMs are initially trained as a general-purpose model and later fine-tuned for specific scenarios. The process starts with vast training data, including structured data (e.g., metadata, tables, and code snippets) and unstructured data (e.g., books, articles, social media posts, and conversational transcripts). Some advanced LLMs also incorporate multimodal data, such as images paired with descriptive text, to handle tasks like image captioning or visual question answering.

The training process involves selecting appropriate learning methods depending on the use case and implementing them on large-scale transformer models (like generative pre-trained transformer (GPT) and bidirectional encoder representations from transformers (BERT)) :

The neural networks deploy self-attention mechanisms (which focus on the relationships between words in a sentence regardless of their position) to understand patterns in the input text, such as human language and its contextual relationships. Fine-tuning is then applied to smaller datasets for specific tasks like question-answering or customer service chatbots.

How do LLMs work?

LLMs process natural language through a series of computational steps that use deep learning, transformer architectures, and vast training data to process and generate human language.

This image provides a simplified representation of a large language model workflow for general understanding. The specific configurations and data flow may vary based on the particular use case.

1. Input processing

LLMs tokenize the input text into smaller units, such as words or subwords, to represent the input data numerically. These tokens are embedded into high-dimensional vectors that capture semantic (word meanings and context) and syntactic information (grammatical structure and word arrangement).

2. Positional encoding

Since transformer models lack inherent sequential awareness, positional encodings are added to the input embeddings. These encodings provide context about the position of tokens in the input text, enabling the model to consider word order.

3. Self-attention computation

The self-attention mechanism calculates the relationships between tokens by assigning weights based on relevance. This allows the model to focus on important words or phrases in the input while processing natural language.

4. Transformer layers

The input data passes through multiple stacked transformer layers, each consisting of self-attention and feed-forward neural networks. These layers learn hierarchical representations of the input text, capturing both local and global dependencies.

For instance, consider a fraud detection system for an e-commerce platform; transformer layers analyze transaction details like: “Purchased a TV for $2,000 at ABCElectronics yesterday using a card ending with 1234.” The transformer layers capture both local dependencies (e.g., “TV” and “ABCElectronics”) and global dependencies (e.g., linking the purchase amount, store, and card details to past transaction patterns). The system detects anomalies, such as high-value purchases from new locations, and flags fraudulent activity in real time.

5. Contextual understanding

As the input data moves through multiple transformer layers, each layer generates hidden states—intermediate text representations that are progressively refined. Using deep learning architectures like feed-forward neural networks, the hidden states, combined with token embeddings, positional encodings, and self-attention outputs, help the model build a deeper contextual understanding of the text. Now, the LLM can interpret the meaning of words and phrases based on their context within the sentence.

6. Output generation

The final layer predicts the most likely next word or token based on the input sequence and learned patterns. Probability distributions over the vocabulary, guided by the training dataset, calculate the likelihood of various words or phrases in the input context and select the one with the highest probability.

7. In-context fine-tuning

In ***specific use cases, pre-trained LLMs undergo fine-tuning task-specific data to refine their output generation. Fine-tuning models adjust weights and improve performance for applications like question answering or text classification. LLMs adapt to user-provided prompts or examples by analyzing patterns and context within the input.

* This is not mandatory for all LLMs and depends on the specific use case.

8. Response delivery

The processed and generated text is decoded from numerical representations back into human language by using a tokenizer. The tokens, which represent words or subwords as numbers or vectors, are mapped back to actual words by referencing the model’s vocabulary. The grammatical and contextual consistency of the output is maintained during the text generation process, where the model samples tokens based on probability distributions learned during training. Finally, a coherent and human-understandable output is presented.

Examples of popular LLMs

Several LLMs have set benchmarks in natural language processing (NLP) through the deployment of advanced transformer models and vast amounts of training data to perform varied tasks.

1. GPT series

The generative pre-trained transformer (GPT) series by OpenAI, including GPT-3 and GPT-4, are among the largest models with hundreds of billions of parameters. These models excel in text generation, question answering, and programming language tasks. GPT models rely on transformer architecture and in-context learning, enabling them to generate human-like text with minimal examples.

2. BERT

Bidirectional encoder representations from transformers (BERT), developed by Google, is a pre-trained language model designed for bidirectional understanding of text. It is widely used for text classification, sentiment analysis, and question-answering. BERT’s focus on bidirectional context improves its ability to capture complex relationships in human language.

3. LLaMA

Meta’s large language model Meta AI (LLaMA) is a collection of foundation models optimized for research and accessibility. With models ranging from smaller scales to very large models, LLaMA aims for efficient performance and customization for specific tasks, using fine-tuning and reinforcement learning.

4. Mistral

Mistral AI focuses on developing open-weight large language models for improved transparency and adaptability. Mistral’s models include dense and mixture-of-experts architectures, offering high performance with lower computational requirements. These models handle a broad range of NLP tasks and are deployed for applications in resource-constrained environments, such as edge devices, low-power systems, and bandwidth-limited setups.

5. Claude

Claude, developed by Anthropic, is an LLM designed for safe and conversational AI interactions. It is built using transformer architectures and fine-tuned with human feedback, Claude focuses on contextual understanding, content generation, and conversational AI and prioritizes safety and alignment with human values.

What are LLMs used for?

LLMs integrate deep learning and transformer architectures to understand and generate natural language, helping you solve complex language processing tasks. Here are some use cases of LLMs:

• Customer support automation— LLMs power AI-driven chatbots and virtual assistants that can instantly respond to your customer inquiries, handle frequently asked questions, and escalate issues to human agents when necessary.
• Sentiment analysis—By examining customer feedback, reviews, and social media posts, LLMs can assess sentiment, identifying whether the tone is positive, negative, or neutral. With sentiment analysis, you can better understand customer perceptions, identify emerging trends, and make data-driven decisions about your product development and marketing strategies.

• Content creation—LLMs analyze and replicate various writing styles, helping you use writing tools to generate contextually relevant content, such as blog posts, articles, and social media captions, while keeping it aligned with your audience.
• Language translation— LLMs distinguish both the semantic meaning and grammatical structure of sentences, making it easier to perform language translation across multiple languages.

Benefits of LLMs

LLMs offer advantages across various industries, driving efficiency, automation, and improved natural language understanding and generation capabilities.

1. Advanced language understanding

LLMs excel at interpreting complex and nuanced human language. By analyzing patterns in vast training datasets, they understand relationships, tone, and meaning, which improves tasks like question answering and natural language generation.

2. Multitasking and scalable automation

With the ability to manage diverse tasks such as code generation and language translation, LLMs simplify task transitions without requiring extensive retraining. The adaptability of these models to in-context learning simplifies transitions across multiple tasks. Their architecture enables them to break down large datasets into smaller components, process them simultaneously across multiple nodes, and generate real-time insights.

For instance, in a financial services firm, an LLM can automate report generation by analyzing raw transaction data, summarizing key trends, and generating compliance reports in multiple languages for global stakeholders. Simultaneously, it can assist data analysts by generating SQL queries based on natural language prompts and explaining the results in a clear, non-technical summary for decision-makers.

3. Improved user experience

LLMs transform user interactions by powering virtual assistants, customer service chatbots, and search engines to deliver instant, context-aware responses. They can troubleshoot issues, automate replies to thousands of technical queries simultaneously, and offer more meaningful support by understanding natural language and user intent.

Challenges of LLMs

While large language models (LLMs) have progressed in natural language understanding, they still face several data, performance, and ethical concerns.

1. Ethical concerns

The outputs that LLMs generate are based on patterns in their training data without inherent understanding or judgment. Training datasets include a mix of curated sources, such as books and research papers, and web-scraped content from the internet, which may introduce biases, inaccuracies, or sensitive information. Despite efforts to filter and preprocess data, it is challenging to eliminate problematic content. This raises ethical responsibility gaps, such as the potential for misuse, the generation of harmful or misleading content, and the replication of sensitive information, which leads to unintended consequences in real-world applications.

2. Technical challenges

LLMs face numerous technical challenges that reflect the complexities of training, fine-tuning, and maintaining these advanced systems. These challenges are rooted in the limitations of deep learning architectures, large-scale datasets, and computational processes:

3. Data bias

LLMs are trained on vast datasets, which may contain inherent biases from the sources they are derived from. The AI biases can influence the model’s behavior, leading to AI hallucinations and skewed or biased outputs in tasks such as text generation or decision-making.

4. Fine-tuning complexity

While LLMs can be fine-tuned for specific tasks, this process requires a careful selection of task-specific datasets and technical expertise. If the fine-tuning data is not thoroughly assessed for quality and safety beforehand, it can amplify biases or reinforce undesirable behaviors in the model, leading to skewed or harmful outputs.

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