A Compact Guide to Large Language Models (Part-1: Introduction of LLM)
Introduction of Large Language Models
Definition of large language models (LLMs)
Large language models are AI systems that are designed to process and analyze
vast amounts of natural language data and then use that information to generate
responses to user prompts. These systems are trained on massive data sets
using advanced machine learning algorithms to learn the patterns and structures
of human language, and are capable of generating natural language responses to
a wide range of written inputs. Large language models are becoming increasingly
important in a variety of applications such as natural language processing,
machine translation, code and text generation, and more.
While this guide will focus on language models, it’s important to understand that
they are only one aspect under a larger generative AI umbrella. Other noteworthy
generative AI implementations include projects such as art generation from text,
audio and video generation, and certainly more to come in the near future.
Brief historical background & development of LLMs
1950s–1990s
Initial attempts are made to map hard rules around languages and
follow logical steps to accomplish tasks like translating a sentence
from one language to another.
While this works sometimes, strictly defined rules only work for
concrete, well-defined tasks that the system has knowledge about.
1990s
Language models begin evolving into statistical models and language patterns start being analyzed, but larger-scale projects are limited by computing power.
2000s
Advancements in machine learning increase the complexity of language models, and the wide adoption of the internet sees an enormous increase in available training data.
2012
Advancements in deep learning architectures and larger data sets lead to the development of GPT (Generative Pre-trained Transformer).
2018
Google introduces BERT (Bidirectional Encoder Representations from Transformers), which is a big leap in architecture and paves the way for future large language models.
2020
OpenAI releases GPT-3, which becomes the largest model at 175B parameters and sets a new performance benchmark for language-related tasks.
2022
ChatGPT is launched, which turns GPT-3 and similar models into a service that is widely accessible to users through a web interface and kicks off a huge increase in public awareness of LLMs and generative AI.
2023
Open source LLMs begin showing increasingly impressive results with releases such as Dolly 2.0, LLaMA, Alpaca and Vicuna. GPT-4 is also released, setting a new benchmark for both parameter size and performance.
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