What is a Large Language Model (LLM)? Application Examples

Large language models are AI-powered deep learning models that can create engaging content on the fly. Trained on large text data, these models understand grammar and context while mimicking human creativity. They revolutionize communication by breaking language barriers and opening up endless possibilities for quickly generating new content.

Facts about the large language model

• OpenAI’s GPT models are the most popular example of generative pre-trained large language models with a large number of parameters.
• Google’s LLM, BERT, leverages its pairwise training for deep understanding of context.
• Google’s LaMDA represents the specialized direction of LLMs, aiming for more natural and fluent dialogues in AI interactions.
• Despite the diversity of LLMs, OpenAI’s GPT series remains the benchmark for accuracy and versatility in language model performance.
• The right large language model for a task depends not only on size, but on its fit with specific industry requirements and use cases.

What is a Large Language Model (LLM)?

Large language models (LLMs) are advanced artificial intelligence models that use deep learning techniques, specifically a subset of neural networks known as transducers.

A large language model uses transducers to perform natural language processing (NLP) tasks such as language translation, text classification, sentiment analysis, text generation and question answering.

LLMs are trained with large sets of data from various sources. Their enormous size characterizes them. Some of the most successful LLMs have hundreds of billions of parameters.

What is the Importance of Large Language Models?

Progress in AI and generative AI is pushing the boundaries of what we once thought was too difficult. LLMs are trained on hundreds of billions of parameters and used to overcome barriers to interacting with machines in a human-like way.

LLMs are useful in problem solving and help businesses with communication-related tasks as they are used to produce human-like text, making them invaluable for tasks such as text summarization, language translation, content creation and sentiment analysis.

Large language models bridge the gap between human communication and machine understanding. Besides the tech industry, LLM applications can also be used in other fields such as healthcare and science. For example, DNA language models (genomic or nucleotide language models) can be used to identify statistical patterns in DNA sequences. LLMs are also used for customer service/support functions such as AI chatbots or conversational AI.

How Large Language Models Work

For an LLM to perform well, it must first be trained on a large volume of data, called a data corpus. The LLM is usually trained with both unstructured and structured data before going through the transforming neural network process.

After pre-training on a large corpus of text, the model can be fine-tuned for specific tasks by training on a smaller dataset relevant to that task. LLM training is primarily done through unsupervised, semi-supervised or self-supervised learning.

Large language models are built on deep learning algorithms called transformer neural networks, which learn context and understanding through sequential data analysis.

The transformer concept was introduced by Ashish Vaswani, Noam Shazeer, Niki Parmar and five other authors in the 2017 paper “Attention Is All You Need”.

The transformer model uses an encoder-decoder structure; it encodes the input and decodes it to produce an output prediction.

Multi-head self-attention is another important component of the transformer architecture, allowing the model to weigh the importance of different tokens in the input when making predictions for a given token. The “multi-head” feature allows the model to learn different relationships between tokens at different locations and levels of abstraction.

Types of Large Language Model

Common types of LLM are as follows:

1. Language Representation Model

Many NLP applications are built on language representation models (LRM) designed to understand and generate human language. Examples of such models are GPT (Generative Pre-trained Transformer) models, BERT (Bidirectional Encoder Representations from Transformers) and RoBERTa. These models are pre-trained on large text corpora and can be fine-tuned for specific tasks such as text classification and language generation.

2. Zero Shot Model

Zero-shot models are known for their ability to perform tasks without specific training data. These models can generalize and make predictions or generate text for tasks they have never seen before. GPT-3 is an example of a zero-shot model. It can answer questions, translate languages and perform various tasks with minimal fine-tuning.

3. Multimodal Model

LLMs were originally designed for text content. However, multimodal models work with both text and image data. These models are designed to understand and render content across different modalities. For example, OpenAI’s CLIP is a multimodal model that can associate text with images and vice versa, making it useful for tasks such as image captioning and text-based image retrieval.

4. Fine-tuned or Domain Specific Models

While pre-trained language representation models are versatile, they may not always perform optimally for certain tasks or domains. Fine-tuned models have undergone additional training on domain-specific data to improve their performance in specific domains. For example, a GPT-3 model can be fine-tuned on medical data to create a domain-specific medical chatbot or to assist in medical diagnosis.

What is the Purpose Behind Large Language Models?

While LLMs are still under development, they can help users with a variety of tasks and serve their needs in various fields, including education, healthcare, customer service and entertainment. Some of the common purposes of LLMs include the following:

• Language translation
• Code and text generation
• Question answering
• Education and training
• Customer service
• Legal research and analysis
• Scientific research and discovery

What is the Difference Between LLM and Artificial Intelligence?

When it comes to artificial intelligence (AI), you can think of it as a big umbrella that covers a wide range of technologies and applications. AI is about building machines or software capable of learning, problem solving and decision making based on data inputs.

As for large language models (LLMs), they are a specific type of AI that focuses on understanding and producing human-like text. LLMs like GPT are trained on billions of parameters using large amounts of text data from various sources. It uses deep learning techniques such as transformative models with attention mechanisms to efficiently process input sequences.

In short, while AI encompasses a broad array of technological marvels designed to mimic human intelligence in multiple domains, LLMs are specialized subsets primarily focused on specializing in natural language processing tasks.

What is the Difference Between NLP and LLM?

NLP, or natural language processing, is a subfield of artificial intelligence (AI) that focuses on enabling computers to understand, interpret and produce human language. It includes various tasks such as text analysis, emotion detection, machine translation and dialog systems. NLP aims to bridge the gap between humans and machines by enabling communication in natural languages.

On the other hand, LLMs, or large language models, are advanced artificial intelligence models designed specifically for high-level NLP tasks. These models are trained on large amounts of text data using deep learning techniques, such as transducer architectures with attention mechanisms. LLMs can grasp context better than traditional NLP methods thanks to their ability to efficiently process billions of parameters.

What is Generative AI vs Large Language Models?

Generative AI is a branch of AI that focuses on creating new content such as images, text, music and even videos. It involves algorithms that can produce outputs based on patterns and structures they learn from training data. Some popular generative AI techniques include Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs) and Recurrent Neural Networks (RNNs).

Large language models (LLMs) are a special type of generative AI that specializes in understanding and generating human-like texts. These models are trained using large amounts of text data to learn grammar, context, style variations and other linguistic nuances. LLMs use advanced deep learning techniques such as transducer architectures with attention mechanisms to efficiently process input sequences.

Top 7 Large Language Models

Below you can find seven important LLMs that stand out:

1. OpenAI GPT

Highly popular in the LLM arena, GPT is a large language model developed by OpenAI. The first version was released in 2018. It has been trained on a variety of internet texts and can generate coherent, contextually relevant sentences by predicting the next word in a given word sequence. GPT-3, released in 2020, had 175 billion parameters and was the first model to produce highly persuasive, human-like text and code based on nuanced instructions.

In late 2022, OpenAI released ChatGPT, based on GPT 3.5 and enhanced with RLHF. ChatGPT was revolutionary in its ability to produce human-like output based on natural language commands. In April 2023, OpenAI released its most capable model, GPT-4, which is currently available as part of the ChatGPT service and also directly via API. GPT-4 significantly surpasses the capabilities of GPT 3.5 in terms of the quality, correctness and contextual relevance of its output.

2. Anthropic Claude

Claude has an impressive context window that can process up to 100 thousand coins simultaneously. This feature enables businesses to process long documents with ease, making it ideal for comprehensive text analysis and comprehension.

3. Meta LLaMA

LLaMA is accessible for both research and commercial purposes under certain conditions. This represents progress towards the democratization of AI. However, commercially fine-tuned models often surpass it due to its extensive optimizations.

4. Microsoft Research ORCA

Orca is an LLM developed by Microsoft and based on a variant of the Meta LLaMA model with 13 billion parameters. Its compact size allows it to run on a simple laptop. The Orca model is designed to surpass existing open source models by copying the logic processing methods used by traditional big language models. With far fewer parameters, Orca competes with the performance levels of GPT-4 and is equal to GPT-3.5 on a variety of tasks.

5. Cohere

Rooted in transformer research, Cohere’s LLM is a versatile and easy-to-use solution for businesses. It focuses on delivering practical language processing capabilities for businesses across different industries.

When choosing an LLM to suit your projects or the needs of your business, you need to consider not only choosing based on popularity, but also finding an LLM that is specifically tailored to the use cases you are targeting. In some cases, using more than one complementary LLM can be useful to unlock the potential synergy between them when combined effectively.

6. Bert

BERT is a model developed by Google. Unlike GPT, which only considers the context to the left of a word, BERT looks at both sides. This bidirectional approach enables BERT to better understand the context of a word, improving its performance in understanding and producing language. BERT has been a major player in various NLP tasks, including question answering and language inference. It has been a core part of the Google Search engine for several years.

7. PaLM

PaLM is a transducer-based model of 540 billion parameters that powers the AI chatbot Bard. Designed to perform reasoning tasks such as coding, math, classification and question-and-answer activities, it is trained on multiple TPU 4 Pods, Google’s dedicated hardware for machine learning. The PaLM model is capable of breaking down complex tasks into more manageable subtasks.

The name PaLM comes from Google’s Pathways research project, which seeks to create a master model that addresses a wide range of applications. There are several iterations of PaLM tuned for precision. Med-PaLM 2 is tailored for life sciences and medical information, while Sec-PaLM is designed for cybersecurity application that helps with accelerated threat analysis.