What Are Large Language Models (LLMs)?

Generative AI has been talked about a lot since ChatGPT came into the picture. Another term that has gained extreme popularity is Large Language Models (LLMs). But what are large language models and what exactly is the difference between Gen AI and Large Language Models? Both these seem to be everywhere and even used interchangeably quite often. This blog offers a brief overview of them separately as well as Generative AI with Large Language Models (LLM).

Introduction To Large Language Models

Artificial Intelligence is quite a broad field and encompasses research into various kinds of problems. Be it ad targeting, autonomous vehicles, weather prediction, chess playing, photo tagging or speech recognition, AI is prevalent everywhere. The field of AI research has always meant work on different topics parallelly. The center of gravity around its progress has certainly shifted over the years.

This introduction to 'Large Language Models' covers aspects of each of these terms individually first. Thereon, this post will give a detailed comparison on Generative AI vs Large Language Models. The goal is to give a clear line of distinction about these two.

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What Are Large Language Models?

Large language models (LLMs) are a form of AI system working with language. In simple context, a researcher responsible for creating an LLM will aim to model language. This would be a simplified but useful digital representation. The word 'large' here points to the movement towards training language models comprising more parameters.

Using more computational power and data for training models with more parameters time and again results in finer performance. In short, cutting-edge language models that are trained today might have manifold more parameters than language models that were trained ten years ago. Consequently, the description contains the word large. Some commonly known and used examples of LLMs are OpenAI's GPT-4, Meta's LLaMA and Google's PaLM.

They are a segment of foundation models that are trained on gigantic amounts of data. This gives them the prowess to understand and generate natural language as well as other kinds of content needed to perform different tasks. LLMs are curated to understand and generate text highly akin to humans. They can infer from context, translate to other languages, generate coherent relevant responses, answer questions, assist in code generation or creative writing tasks, and summarize text.

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How Do Large Language Models Work?

A major question here is how do Large Language Models work. LLMs are constructed upon a transformer model. They work by receiving an input, moving on to encoding it and then finally decoding it to generate an output prediction. A lot of training is required for an LLM to receive text input and generate a correct output prediction. Fine tuning is also an essential aspect for performing specific tasks.

• Training:LLMs are pre-trained via gigantic textual datasets from different sites such as GitHub, Wikipedia and others. These data sets comprise trillions of words with their quality affecting the language model's performance. This is the stage where the LLM moves towards unsupervised learning. In short, it processes the data sets that are fed to it without any additional specific instructions. The LLM's AI algorithm may learn the definition of words and shared relationships between words during this process.

• Fine-tuning:For an LLM to perform a specific task like translation, it should be fine-tuned to be able to perform that particular activity. Fine tuning helps in optimizing the performance associated with specific tasks. Prompt-tuning also plays a function similar to fine-tuning. Prompt tuning is when a model is trained to perform a certain task via few-shot prompting or even zero-shot prompting. A prompt refers to an instruction that is given to an LLM. Few-shot prompting is one that trains the model to predict outputs via using examples. Zero shot prompting, alternatively, does not make use of examples for teaching the model about the how-to's of responding to inputs.

Overview of Generative AI

So, what is Generative AI? It's basically quite a broad term that is being used for any AI system fulfilling the primary function of generating content. Its functionality is in contrast to AI systems performing other functions. Most AI systems classify data, group data or choose actions. Gen AI, on the contrary, performs more creative tasks. There are many common examples of Gen AI systems around us today. These include Large Language Models (like GPT-4, Claude or PaLM), image generators (like Midjourney or Stable Diffusion), audio generation tools (like VALL-E or resemble.ai) or code generation tools (like Copilot). The term 'Generative AI' lays solid emphasis on the content-creating function served by these systems. It's a comparatively intuitive term that encompasses different kinds of AI that have rapidly progressed in recent times.

Generative AI vs. Large Language Models- A Detailed Comparison

Now that the blog has covered Generative AI with Large Language Models individually, there must be questions about what differs them. Let's get into the depths of Generative AI vs Large Language Models for a detailed comparison between these two. Both are aspects of artificial intelligence and rapidly making rounds in the tech world.

Wrap-Up

This blog taps into topics around Generative AI and what are Large Language Models. It first discusses them individually for a base and then comprises how both are different from one another. Each of these are aspects of artificial intelligence, which has recently taken a turn towards rapid upward movement. Both are integral for having a career in this sector.

FAQs for What Are Large Language Models?

Q1. What is the difference between Generative AI and AI?

Generative AI can be termed as a sub-field of AI. The former is trained on huge data sets.

Q2. Is NLP a large language model?

Large language models can be termed as a sub-field of natural language processing (NLP).

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