According to Stanford, in 2023, 20-30% of responses from large language models contained errors or 'hallucinations.' This means that every third fact could have been fabricated!
Definition in simple words
• The accuracy of AI is how closely the answer aligns with real data.
• Hallucination is when a neural network invents non-existent facts because it couldn't find them in its training or 'guessed' them itself.
Why is this needed
• Accuracy is critical for medicine, finance, and law.
• Imagine a doctor asking AI about medication dosage, and it 'invented' the numbers. The consequences are obvious.
Limitations / downsides
• AI does not understand facts, it 'guesses' the likely continuation of the text.
• The more complex and niche the question, the higher the chance of error.
• Different models have different balances: ChatGPT is better at coding, Claude at long texts, Grok at sarcastic responses, but all can make mistakes.
Examples / visualization
• In 2023, Google Gemini (then Bard) 'hallucinated' a fact about the James Webb telescope, saying it took the first picture of an exoplanet. This turned out to be false, and Google's stock fell by $100 billion.
• ChatGPT can confidently invent 'non-existent' scientific articles if asked for references.
• Claude from Anthropic sometimes provides accurate legal analyses but can get 'confused' with dates.
📊 A study from Stanford Center for Research on Foundation Models showed:
• GPT-4 makes mistakes less often (~3–5% in math and coding tests).
• GPT-3.5 — up to 20%.
• Open-source models (LLaMA, Mistral) — from 15 to 30% depending on the task.
. Conclusion
AI provides a likely answer, not a guaranteed accurate one. For everyday questions — okay. For medicine, science, or finance — a human verifier is needed.
Do you trust AI 100% or do you always check its answers?
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