LLM Primer Part 2

 

This blog is part 2 of a primer about LLM (Large Language Models), which is the basis for AI (artificial intelligence) chatbots that are raging throughout the Worldwide Web.

Please see the original source content for the information in this blog. This is a high-level summary of a much fuller summary at HTTP://www.databricks.com.

The following is a guide to understanding frequently asked questions about LLMs.

Frequently Asked Questions

What are large language models (LLM) and how do they work?

LLMs are advanced artificial intelligence (AI)  and systems that take input to generate human-like responses.

If they've been around for so many years, why are they just surfacing?

• Recent advancements have brought the spotlight to generating AI and LLMs. (See the last blog before this for details)
• ChatGPT opened the door for everyone on the internet to access LLMs.
• As time has progressed personal computer capabilities offer better graphics, and better data processing allowing researchers the ability to train higher-functioning LLMs.
• Because we get better at collecting data, such as Wikipedia's stringent capabilities, LLMs have dramatically improved.

What are organizations using LLMs for?

Content generation

Chatbots and virtual assistants

Code generation and debugging

Sentiment Analysis

Text classification and clustering

Language Translation

Summarization and paraphrasing

Recently, a German disc jokey create a "singer" who has recently signed a record contract with Warner Entertainment --- "Her" name is Noonoouri. (https://www.independent.co.uk/tech/warner-music-central-europe-skims-b2403503.html)

Please note: Most LLMs are not trained to be fact machines. They have access to database knowledge but may not know about recent facts, so it is always important to fact-check everything.

# # # #