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How to Solve LLM Hallucinations
The Necessary Step to AI Revenue
But what you get back is generated based on the input, and due to the probabilistic functions in the design, the output is 'generated' and can appear to give you detail on topics that were initially part of the dataset, but abstracted away into an embedding space inside the model. All of these techniques work on the principle that generalised knowledge, when trained with sufficient data or in the correct way, increase accuracy, decrease hallucinations, and offer a minimal loss function (as described above). The method is itself interesting, but offers a good question mark that might define the future of how machine learning compute profiles could change - perhaps sizably, in the same way transformers did compared to previous convolutional neural networks.
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