Raw LLM Responses

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No, not because of reasons. If you provide an LLM with sufficient context, you can get the exact opposite answer by conditioning the model with one-sided argumentative input and asking it to summarise the argumentative balance. In other words, if you constrain the LLM to your arguments, you can move the needle in the direction you want. That's what Knowles did here. ChatGPT was being given arguments in favour of God's existence. Had Knowles provided nothing but arguments against God's existence, the percentage would have fallen.
youtube 2026-01-01T17:5… ♥ 1
Coding Result
DimensionValue
Responsibilityuser
Reasoningmixed
Policynone
Emotionmixed
Coded at2026-04-27T06:24:59.937377
Raw LLM Response
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