Demystifying ChatGPT: How it works, limitations and future
ChatGPT. I’m sure you have heard about this chatbot that can have natural conversations and generate high-quality content on demand. But what exactly are ChatGPT and other similar large language models? How do they work and what are their capabilities and limitations? In this post, I will explain all about these hot new AI tools in simple terms.
What is ChatGPT and What are Large Language Models?
ChatGPT is an example of a large language model (LLM). LLMs are AI systems trained on massive text data to predict upcoming words and generate human-like text. In essence, they learn linguistic patterns by analyzing huge volumes of books, articles, and other content to become skilled at writing naturally in response to prompts. ChatGPT specifically is an LLM tuned to have natural conversations and produce high quality written content on a wide range of topics.
Types of Large Language Models
There are a few types of large language models:
- Auto-regressive — Predict next token based on previous tokens
- Auto-encoding — Reconstruct input text after compressing it
- Sequence-to-sequence — Map input to output text
ChatGPT is an auto-regressive model, meaning it predicts the next word based on the previous sequence of words. Other popular auto-regressive LLMs are GPT-3, Codex and PaLM.
LLM Applications in Business
LLMs like ChatGPT have tremendous potential to transform businesses by automating content creation and other cognitive tasks. Some ways businesses can benefit from LLMs:
- Generate marketing copy, emails, social media posts
- Code automation and software development
- Customer support automation
- Market research and analysis
- Recruiting screening
As LLMs continue improving, their applications will expand even further.
How Does ChatGPT Work? — Step-By-Step
Now that we know what ChatGPT is, let’s demystify how ChatGPT works under the hood:
- 1. Ingest Massive Training Datasets
The foundation of ChatGPT is the gigantic dataset it was trained on containing over 570GB of text data. This includes all types of written content including books, Wikipedia entries, web pages, publications and more.
It contains text covering a wide variety of topics, writing styles and formats give the model broad exposure to understand real world written content.
- 2. Process and Clean Data
Before the collected text data can be used for training, it must be cleaned and processed. This involves:
- Removing inconsistencies like incorrect formatting
- Normalizing punctuation, spelling variations
- Structuring texts into input/output pairs e.g. question/answer
Cleaning prepares the data for the model to better recognize pattern
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