Large Language Models (LLMs) became a hot topic since the launch of OpenAI’s LLM “ChatGPT” in November 2022. The unparalleled success of ChatGPT (more than 1 million users in 5 days) sparked intense interest in understanding the maturity level of artificial intelligence (AI) and how companies can leverage it to transform their business.

So far, LLMs have shown great adaptability and contextual understanding. This has made them invaluable tools for companies and fueled the race for AI business solutions.

In this article, we gathered all the information you need to understand LLMs and bridge the gap between technological complexity and practical business applications.

LLMs Explained

1. Brief History

The history of Language Models traces back to the 1950s when the Georgetown–IBM experiment showcased early attempts at machine translation from Russian to English.

The concept of LLMs took shape in the 1960s with the creation of Eliza, the first chatbot, paving the way for Natural Language Processing (NLP) research. Until the 1990s, rule-based systems dominated language modeling, but the field gained momentum with statistical language models analyzing large datasets.

Fast forward to 2011, advanced features like word embeddings in Google Brain marked a turning point, followed by the emergence of transformer models in 2017 with GPT (Generative Pre-trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers) leading the way.

The focus from 2018 onward was on building larger models which led to the introduction of GPT-4 in March 2023, pre-trained on more than a trillion parameters and demonstrating the rapid evolution of LLMs.

2. What are LLMs

LLMs are foundation models (AI models) trained on large textual data. They can understand and generate human-like text via deep learning algorithms.

The primary role of a language model is to determine the likelihood of a word appearing after a specified input in a sentence. For instance, in the phrase "The rose is ……..," the model predicts that "red" is the most probable next word. The language model becomes adept at understanding the contextual relationships between words by analyzing extensive examples.

During training, the model learns to discern various patterns between words, forming the basis of what is called a pre-trained language model.

3. Evolution of Language Models

As we trace the historical development of LLMs, here are the language model types you need to know:

Rule-Based Models

In the early stage of language processing, rule-based models were prevalent. These systems relied on predefined sets of rules to understand and generate language.

While effective for simple tasks, these models were very rigid and limited in fluency. They lacked the flexibility to handle the nuances and complexities of natural language, limiting their practicality in real-world applications.

Statistical Language Models

With the rise of statistical approaches, models began analyzing large datasets to identify patterns and relationships within language. They were based fully on probabilities and statistics.

However, they still struggled to capture the full richness of human communication which made them suitable for certain tasks but less adept at more nuanced language understanding.

Neural Language Models

These are language models powered by artificial neural networks that teach computers to process data by mirroring the cognitive processes of the human brain.

Thanks to the advent of deep learning, these models demonstrated enhanced capabilities in understanding and generating language.

Transformer Models

Transformers, introduced in the GPT-1 model, revolutionized language processing by capturing long-range dependencies in text through self-attention mechanisms. Self-attention means that it allows each word to be aware of the importance of every other word in order to capture rich contextual relationships in language.

Transformers laid the foundation for subsequent LLMs, offering improved efficiency in handling sequential data and enabling more accurate language predictions.

Popular LLMs

There are many LLMs currently available. These are the popular models:

1. GPT-4 (Generative Pre-trained Transformer 4)

Developed by OpenAI, GPT-4 is one of the most powerful language models, boasting more than one trillion parameters. It follows a transformer architecture and is pre-trained on a diverse range of internet text.

2. BERT (Bidirectional Encoder Representations from Transformers)

BERT was developed by Google. It introduced bidirectional context understanding, capturing context from both directions in a sentence. It has significantly impacted natural language processing tasks.

3. T5 (Text-To-Text Transfer Transformer)

Developed by Google, it is designed with a unified text-to-text framework, treating all NLP tasks as converting one kind of text to another. It can handle various language tasks like translation, summarization, question-answering, and more, using a consistent and generalized approach.

4. RoBERTa (Robustly optimized BERT approach)

RoBERTa is a refined version of BERT developed by Facebook AI, addressing some of BERT's limitations through modifications in training objectives and hyperparameters. It was trained longer over more data with improved optimization.

RoBERTa enhances performance in various language tasks, such as sentiment analysis and named entity recognition by improving the model's robustness and training efficiency.

5. LLaMA 2 (Large Language Model Meta AI)

This is Meta's open-source 70 billion parameter LLM. It’s a general-purpose cross-lingual model that covers more than 100 languages with conversational abilities in multiple domains.

Llama 2 reflects substantial upgrades over the first generation. Its pre-trained models are trained on 2 trillion tokens, they have double the context length, and its fine-tuned models have been trained on over 1 million human annotations [1].

6. PaLM 2 (Pathways Language Model)

Pathways Language Model version 2 from Google DeepMind has 540 billion parameters. It uses an innovative hierarchical architecture called Pathways to model long-term context dependencies in text.

It has demonstrated stellar performance on numerous benchmarks and real-world applications from question answering to code generation.

7. Gemini

Gemini is Google’s latest large language model released in December 2023. It is a new line of multimodal foundation models and Google’s largest model, surpassing PaLM in scale.

It is built from the start to integrate different data modes like text, images, audio, and video, outperforming the capabilities of other prominent models such as ChatGPT and GPT4-Vision on many evaluations.

This model comes with three variants differentiated by scale - Gemini Ultra, Gemini Pro, and Gemini Nano.

8. Claude 2

A small but powerful model from Anthropic with a strong emphasis on conversational safety. This is achieved through methods like Constitutional training, utilizing carefully curated human self-talk data to ensure value alignment. It underpins Anthropic's chatbot assistant.

Claude 2 enhanced the model’s response latency by bringing it down to 1-2 seconds from 10 seconds, enabled more personalization, and showed better context-aware conversational skills compared to Claude 1 [2].

9. BARD (Bidirectional and Auto-Regressive Decoder)

This LLM was also developed by Google and introduced publicly in February 2023. It is known as Google's version of ChatGPT. It is based on PaLM 2 and originated from Google's LaMDA (Language Model for Dialogue Applications) which builds on top of the Transformer network architecture and is specifically focused on dialog skills.

10. Mixtral 8x7B

Introduced in December 2023 by Mistral AI, a French AI company, Mixtral 8x7B assembles diverse, specialized AI models, selecting the best for each text task. This open-source model is a “mixture of experts” (MoE) model with open weights. It surpassed many other learning models like Llama 2 70B, enabling 6x faster response, and is reported to match or outperform GPT-3.5 on many benchmarks [3].

Language Model Size: Large vs Small

The key differences between LLMs and SLMs (Small Language Models) reside in the following factors:

1. Model Size

In this context, "size" means the number of parameters or weights in the model. LLMs refer to models with a substantial number of parameters, indicating the complexity and depth of the model.

LLMs excel in handling vast and diverse datasets, allowing for a broader understanding of language and context. They are particularly effective in generating detailed and contextually rich outputs.

On the other hand, SLMs have fewer parameters, making them less complex compared to their larger counterparts. They may not have the same depth of understanding as larger ones but they are often more computationally efficient. They are suitable for specific tasks where a more lightweight solution is preferable.

2. Computational Resources

Building and training LLMs require significant computational resources and infrastructure due to the sheer volume of parameters. These models are well-suited for organizations with robust computing capabilities.

Smaller models demand less computational power which makes them more accessible for entities with limited resources. They are a practical choice for businesses seeking efficient solutions for simpler language tasks.

The computational budgets for developing LLMs run into millions of dollars to train models with hundreds of billions of parameters on industrial-sized clusters of thousands of GPUs. Much smaller teams can build smaller domain-focused models with dozens of millions of parameters.

3. Architecture

LLMs utilize advanced architectural features like sparsely gated activations, mixture-of-experts layers, and transformers that preserve position information through attention. These innovations allow scaling to billions of parameters.

In contrast, smaller models opt for simpler architectures lacking the intricate mechanisms for parallelization across GPU clusters.

4. Transfer Learning

A hallmark of LLMs is their ability to transfer learning from broad pre-training to specialized downstream tasks via fine-tuning. Smaller models often need to be trained from scratch task-by-task.

How Do LLMs Learn

Language Models operate on the principle of understanding and generating human-like language. They leverage neural networks, specifically transformer architectures, to process and predict sequences of words in a given context.

They utilize a few key learning methods to build their deep understanding of language from data:

1. Unsupervised or Self-Learning

This is the primary technique for LLM development. The model is exposed to vast corpora of natural text, usually from published books and web data. There are no human-labeled examples required. The model must simply predict masked words and sentences based on the surrounding context. This rewards linguistic comprehension and generation ability.

2. Supervised Learning

Sometimes LLMs are further fine-tuned on smaller human-labeled datasets for specialized tasks like question answering or summarization. For example, taking sample paragraphs paired with answerable questions and answers. This adds explicit supervision for the target applications on top of the self-supervised capability.

3. Semi-Supervised Learning

Bridging the two techniques above, this approach combines the use of both labeled and unlabeled datasets to train an AI model. The small portion is labeled by humans and then used as a base to label the large unlabeled dataset.

4. Reinforcement Learning From Human Feedback (RLHF)

LLMs conduct trial-and-error directly in an environment to further tune behavior. For instance, a conversational AI can practice having discussions and get feedback from humans on the quality. This allows adapting language use towards better outcomes in applications.

5. Transfer Learning

Here, a model is first pre-trained on a large dataset to understand language patterns and then fine-tuned on a small specific dataset to specialize in a particular task or domain.

LLM Applications and Tasks

In essence, the versatility of LLMs extends across a spectrum of tasks and applications:

1. Information Extraction

LLMs excel in extracting relevant information from unstructured data, automating the process of identifying key details from documents, articles, or websites. This aids in efficient data analysis and knowledge extraction.

2. Text categorization

LLMs categorize large volumes of text into predefined categories or tags, streamlining the content organization and facilitating targeted information retrieval. This is convenient for content management and information structuring.

3. Language Translation

This was one of the early applications of language models. Leveraging their understanding of context, LLMs are instrumental in automatic language translation.  

Modern LLMs capture nuances for more natural translations to enable accurate and contextually appropriate translations to enhance cross-language communication.

4. Summarization

LLMs are able to summarize lengthy documents or articles, distilling key information and presenting it concisely. This is suitable for quick information retrieval and decision-making.

5. Text Generation

LLMs can produce original coherent text on a wide range of topics, assist human writers, or fully automate content creation.

6. Sentiment Analysis

LLMs analyze text to determine sentiment to provide businesses with insights into customer opinions, feedback, and market sentiment. This is valuable in reputation management, product development, chatbots, financial markets, and ESG (Environment, Social, Governance) performance.

7. Named Entity Recognition (NER)

LLMs identify and classify entities such as names, companies, and locations in text which allows extracting structured facts and facilitates data organization.

8. Conversational AI

LLMs power chatbots and virtual assistants to sustain long, flowing, natural-sounding, and context-aware conversations on open-ended topics with users. This application is useful in enhancing customer service and improving satisfaction.

9. Question-Answering System

LLMs can process and answer user queries by understanding context and retrieving accurate answers from a database or document. This is useful to address FAQs and knowledge sharing.

10. Smart Search

LLMs improve search engine capabilities by understanding user intent and delivering more relevant results based on context and semantics.

11. Code Generation

LLMs assist in generating code snippets based on natural language descriptions, streamlining the coding process for developers.

12. Speech-to-Text and Text-to-Speech

LLMs contribute to accurate speech-to-text conversion and natural-sounding text-to-speech synthesis, enhancing accessibility and communication.

13. Personalization

LLMs enable personalized content recommendations and user experiences by understanding individual preferences and behaviors. Their output is tailored to match the target audience's specific needs.

14. Document Review and Analysis

LLMs assist in reviewing and analyzing financial documents, contracts, or legal texts, to automate the extraction of critical information and ensure compliance.

How to Use LLMs

The choice of methodology depends on the specific needs and resources of your business. There are five approaches:

1. Prompt Engineering

Prompt engineering involves designing specific instructions or queries (prompts) to guide the LLM's generation of desired outputs.

This approach is effective when you need to have a precise control over the model's output. It allows you to shape responses by carefully constructing prompts tailored to the task or information you seek.

2. Model Fine-tuning

Fine-tuning entails training a pre-existing LLM on domain-specific data or labeled datasets to enhance its performance for a particular task.

This methodology is beneficial when adapting a general-purpose LLM to address specific business needs, ensuring the model becomes more proficient in tasks relevant to your company.

3. RAG (Retrieval-Augmented Generation)

RAG is a learning technique that combines information retrieval with LLMs, allowing the model to access external knowledge during the generation process.

It is highly beneficial in scenarios that demand frequently updated, domain-specific knowledge, such as answering questions about a company's products and services using the latest documentation available on its website.

4. Build from Scratch

Building a language model from scratch involves training a model entirely anew, often with a specific architecture or hyperparameters tailored to the organization's requirements.

This approach is suitable when there's a need for a highly customized model, tailored to unique language patterns or industry-specific contexts. It is very expensive and requires extensive amounts of data.

5. Knowledge Distillation

Knowledge distillation transfers knowledge from a large pre-trained model (teacher) to a smaller model (student), often with reduced computational requirements.

This methodology is useful for deploying efficient and resource-friendly models while retaining the knowledge acquired by more substantial pre-trained models. It's especially beneficial in scenarios with limited computational resources and enables large-scale deployment.

Final thoughts

Today, embracing LLMs is a strategic imperative if you’re looking to stay ahead of your competitors. LLMs are not mere technological products; they are dynamic tools that have the power to redefine and transform your business.

With their deep understanding of language and versatile applications, LLMs are invaluable tools for your business to improve productivity and increase profitability.

To have a deeper understanding of how you can strategically integrate LLMs into your business, schedule a discovery meeting with our team today, and let’s discuss how we can accompany you in your AI journey.