Raw LLM Responses

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@mrSam3ooo my (very uninformed) hypothesis would be something like the way that words are connected to each other based on their context (different weights in an informational sense), this can be applied too to speech. Large language models are very good at dealing with context, so naturally it would follow that the word "can" in most contexts is the modal verb, not the noun "can" (container) - these 2 different instances of "can" hold different values in the LLM. Then knowing what the informational values are in the LLM comes from the training data, perhaps millions of hours of speech have trained ChatGPT to be able to speak to such a good level. And the neural networks/reinforced learning have helped define the contexts of when they're used too. So in essence, the programming is more the dataset, learning, and reinforcement of learning than what it sounds like. P.S. I'm not a computer scientist, or AI specialist of any kind! Just an observer.
youtube AI Moral Status 2024-07-26T22:5… ♥ 1
Coding Result
DimensionValue
Responsibilitynone
Reasoningunclear
Policyunclear
Emotionindifference
Coded at2026-04-27T06:24:59.937377
Raw LLM Response
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