A Compact Guide to Large Language Models (Part-2: Understanding LLM)
Understanding Large Language Models
What are language models and how do they work?
Large language models are advanced artificial intelligence systems that take
some input and generate humanlike text as a response. They work by first
analyzing vast amounts of data and creating an internal structure that models
the natural language data sets that they’re trained on.
Once this internal
structure has been developed, the models can then take input in the form of
natural language and approximate a good response.
If they’ve been around for so many years, why are they just now
making headlines?
A few recent advancements have really brought the spotlight to generative AI
and large language models:
ADVANCEMENTS IN TECHNIQUES
Over the past few years, there have been significant advancements in the techniques used to train these models, resulting in big leaps in performance. Notably, one of the largest jumps in performance has come from integrating human feedback directly into the training process.
INCREASED ACCESSIBILITY
The release of ChatGPT opened the door for anyone with internet access to interact with one of the most advanced LLMs through a simple web interface. This brought the impressive advancements of LLMs into the spotlight, since previously these more powerful LLMs were only available to researchers with large amounts of resources and those with very deep technical knowledge.
GROWING COMPUTATIONAL POWER
The availability of more powerful computing resources, such as graphics processing units (GPUs), and better data processing techniques allowed researchers to train much larger models, improving the performance of these language models.
IMPROVED TRAINING DATA
As we get better at collecting and analyzing large amounts of data, the model performance has improved dramatically.
So what are organizations using large language models for?
Here are just a few examples of common use cases for large language models:
CHATBOTS AND VIRTUAL ASSISTANTS
One of the most common implementations, LLMs can be used by organizations to provide help with things like customer support, troubleshooting, or even having open-ended conversations with userprovided prompts.
CODE GENERATION AND DEBUGGING
LLMs can be trained on large amounts of code examples and give useful code snippets as a response to a request written in natural language. With the proper techniques, LLMs can also be built in a way to reference other relevant data that it may not have been trained with, such as a company’s documentation, to help provide more accurate responses.
TEXT CLASSIFICATION AND CLUSTERING
The ability to categorize and sort large volumes of data enables the identification of common themes and trends, supporting informed decision-making and more targeted strategies.
SENTIMENT ANALYSIS
Often a hard task to quantify, LLMs can help take a piece of text and gauge emotion and opinions. This can help organizations gather the data and feedback needed to improve customer satisfaction.
LANGUAGE TRANSLATION
Globalize all your content without hours of painstaking work by simply feeding your web pages through the proper LLMs and translating them to different languages. As more LLMs are trained in other languages, quality and availability will continue to improve.
CONTENT GENERATION
Start with a detailed prompt and have an LLM develop an outline for you. Then continue on with those prompts and LLMs can generate a good first draft for you to build off. Use them to brainstorm ideas, and ask the LLM questions to help you draw inspiration from.
SUMMARIZATION AND PARAPHRASING
Entire customer calls or meetings could be efficiently summarized so that others can more easily digest the content. LLMs can take large amounts of text and boil it down to just the most important bytes.
More Blogs
Leave a Reply