AI Fundamentals Part 4 : Introduction to LLMs(Large Language Models)

What is Large Language Models (LLMs)?

Artificial Intelligence (AI) is the simulation of human intelligence in machines that are programmed to think and learn. Large Language Models (LLMs) are a type of AI that processes, understands, and generates human-like text based on vast datasets.

History of Large Language Models

- LLMs have a fascinating history that dates back to the 1960s with the creation of the first-ever chatbot, Eliza.
- Over the years, several significant innovations have propelled the field of LLMs forward. One such innovation was the introduction of Long Short-Term Memory (LSTM) networks in 1997, which allowed for the creation of deeper and more complex neural networks capable of handling more significant amounts of data.
- Another pivotal moment came with Stanford’s CoreNLP suite, which was introduced in 2010. The suite provided a set of tools and algorithms that helped researchers tackle complex NLP tasks such as sentiment analysis and named entity recognition.
- In 2011, Google Brain was launched, providing researchers with access to powerful computing resources and data sets along with advanced features such as word embeddings, allowing NLP systems to better understand the context of words.
- Google Brain’s work paved the way for massive advancements in the field, such as the introduction of Transformer models in 2017.
- The transformer architecture enabled the creation of larger and more sophisticated LLMs such as OpenAI’s GPT-3 (Generative Pre-Trained Transformer) which served as the foundation for ChatGPT and a legion of other incredible AI-driven applications.

In recent years, solutions such as Hugging Face and BARD have also contributed significantly to the advancement of LLMs by creating user-friendly frameworks and tools that enable researchers and developers to build their own LLMs.

Types of Large Language Models(LLMs)

Three major categories can be used to categorize large language models (LLMs): multimodal, fine-tuning, and pre-training models.
- Pre-training models: with large data sets, such as GPT-3/GPT-3.5, T5, and XLNet, are able to acquire a broad knowledge base of linguistic patterns and structures. These models are quite good at producing grammatically sound and coherent content on a wide range of subjects. They serve as a foundation for additional instruction and task-specific fine-tuning.
- Fine-tuning models: models such as ALBERT, RoBERTa, and BERT are trained beforehand on a big dataset and then adjusted for a particular job on a smaller dataset. For applications such as text classification, question answering, and sentiment analysis, these models work incredibly well. When task-specific language models are required in industrial applications, they are frequently employed.
- Multimodal models: to produce more reliable language models, models such as CLIP and DALL-E mix text with additional modalities like images or video. These models are able to provide text descriptions of images or even produce images from textual descriptions since they are able to comprehend the links between images and words.

Applications of Large Language Models

Large Language Models (LLMs) have demonstrated remarkable performance in a wide range of natural language processing (NLP) tasks like :
- Content Generation: Articles, stories, code.
- Translation: Converting text from one language to another.
- Customer Service: Automated responses and support bots.