We can't base our entire understanding of [large language models] on their inputs and outputs alone. It's important to understand why they have a specific output.
The internal mechanics of how an AI model arrives at those answers aren't visible, leading many researchers to describe them as 'black box' systems that require deeper investigation.
Understanding model bias and decision-making is crucial, especially in high-impact situations like medical diagnoses, to ensure the performance of AI systems.
By analyzing the combinations of activated artificial neurons, researchers can map features within neural networks, which could impact how AI models behave.
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