1 Large language models: The power of AI

 

This chapter covers

  • Introducing large language models
  • Understanding the intuition behind transformers
  • Exploring the applications, limitations, and risks of large language models
  • Surveying breakthrough large language models for dialogue

On November 30, 2022, San Francisco–based company OpenAI tweeted, “Try talking with ChatGPT, our new AI system which is optimized for dialogue. Your feedback will help us improve it” [1]. ChatGPT, a chatbot that interacts with users through a web interface, was described as a minor update to the existing models that OpenAI had already released and made available through APIs. But with the release of the web app, anyone could have conversations with ChatGPT, ask it to write poetry or code, recommend movies or workout plans, and summarize or explain pieces of text. Many of the responses felt like magic. ChatGPT set the tech world on fire, reaching 1 million users in a matter of days and 100 million users two months after launch. By some measures, it’s the fastest-growing internet service ever [2].

Evolution of natural language processing

 

The birth of LLMs: Attention is all you need

 
 
 

Explosion of LLMs

 
 
 
 

What are LLMs used for?

 

Language modeling

 
 
 

Question answering

 
 

Coding

 
 

Content generation

 
 
 

Logical reasoning

 
 
 
 

Other natural language tasks

 
 
 

Where do LLMs fall short?

 

Training data and bias

 
 
 

Limitations in controlling machine outputs

 

Sustainability of LLMs

 

Revolutionizing dialogue: Conversational LLMs

 
 

OpenAI’s ChatGPT

 
 
 

Google’s Bard/LaMDA

 
 
 
 

Microsoft’s Bing AI

 
 

Meta’s LLaMa/Stanford’s Alpaca

 
 
 
 

Summary

 
 
sitemap

Unable to load book!

The book could not be loaded.

(try again in a couple of minutes)

manning.com homepage