How ChatGPT learns and why it sometimes gets things wrong
Exploring the sources of ChatGPT's knowledge and the reasons behind its occasional inaccuracies
(Image credit: Shutterstock / Ryzhi)
ChatGPT, OpenAI's large language model (LLM), often appears to know everything—but it doesn't. While it can generate fluent, informed responses, it lacks true understanding and sometimes produces incorrect or fabricated information, a phenomenon known as hallucinating.
How ChatGPT Works
At its core, ChatGPT functions like an advanced autocomplete tool. It predicts the next word in a sequence based on patterns learned from vast amounts of training data, including books, articles, websites, and public forums like Reddit. However, its knowledge is limited to the data it was trained on, and some models don't access real-time information unless they have browsing capabilities.
Sources of ChatGPT's Knowledge
ChatGPT's training data includes:
- Books and academic papers
- Publicly available websites and forums
- Wikipedia pages
- Open-source code repositories
Despite its broad training, ChatGPT hasn't read private or restricted content. However, ethical concerns persist about the use of shadow libraries and copyrighted material in AI training.
Why ChatGPT Sounds Convincing
ChatGPT excels at mimicking human language patterns, making its responses sound natural and authoritative. This fluency, combined with its ability to remember past conversations, creates the illusion of deep knowledge. Yet, it can also reflect biases and inaccuracies present in its training data.
Limitations and Risks
- Outdated Information: Some models have knowledge cutoffs (e.g., GPT-4o's data ends in June 2024).
- Confidence Without Accuracy: ChatGPT often delivers answers with unwavering confidence, even when incorrect.
- Bias Propagation: It may replicate societal biases found in its training data.
Using ChatGPT Wisely
While ChatGPT is a powerful tool for drafting, summarizing, and brainstorming, users should:
- Verify facts independently
- Be aware of its limitations
- Understand that it doesn't "think" like a human
For more on AI hallucinations, visit this link.
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About the Author
Dr. Emily Wang
AI Product Strategy Expert
Former Google AI Product Manager with 10 years of experience in AI product development and strategy formulation. Led multiple successful AI products from 0 to 1 development process, now provides product strategy consulting for AI startups while writing AI product analysis articles for various tech media outlets.