Methods for identifying when an AI model generates false or unsupported claims.
Methods for identifying when an AI model generates false or unsupported claims. Approaches include fact-checking against knowledge bases, consistency checks across multiple generations, and specialized detection models. An active research area because hallucinations remain one of LLMs' most dangerous failure modes.
When an AI model generates confident-sounding but factually incorrect or completely fabricated information.
Connecting an AI model's outputs to verified, factual information sources.
A mathematical function applied to a neuron's output that introduces non-linearity into the network.
An optimization algorithm that combines the best parts of two other methods — AdaGrad and RMSProp.
Artificial General Intelligence.
The research field focused on making sure AI systems do what humans actually want them to do.
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