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Parameters are the weights and biases in a neural network that the model adjusts during training to minimize error in predictions.
Hallucination refers to instances where the model produces outputs that are factually incorrect or not grounded in reality, despite sounding plausible.
This technique prompts the model to articulate its thought process step-by-step, leading to more accurate and transparent outputs.