What are AI Hallucinations?
AI tools like Google's AI Overviews and ChatGPT sometimes give bizarre, inaccurate answers—this is called "hallucination".
Examples include recommending glue in pizza sauce or eating rocks and drinking urine.
Hallucinations happen when AI models are unsure or lack training data for specific queries but still respond confidently.
Why Do AI Models Hallucinate?
AI doesn’t truly “understand” language—it relies on statistical patterns from training data.
They often struggle with negation (e.g., generating images of a room without elephants still included elephants).
Lack of training examples with negation contributes to these issues.
AI models tend to guess based on similar patterns seen during training, even when it leads to false information.
Challenges with Reliability
AI reliability is measured by consistency (same output for similar input) and factuality (providing correct information or admitting uncertainty).
Current AI models often fail in factuality, confidently giving wrong information instead of saying “I don’t know.”
Some AI models may be tested with data they were trained on, which inflates their scores but reduces real-world accuracy.
How to Reduce Hallucinations
Modern models like GPT-4 hallucinate less, especially for common queries.
Experts suggest training on specific tasks (small language models), not broad data.
Retrieval-Augmented Generation (RAG) helps by guiding AI to use reliable sources.
Curriculum learning (gradually increasing training complexity) can improve learning accuracy.
Despite improvements, experts agree human oversight is still needed to catch errors AI might make.
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