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Dan's avatar

Hmmm so I've been mulling this too: LLMs for example do predictive modeling based on what is most likely to come next averaged across all the different training examples. They sometimes make stuff up and sometimes get things wrong. It's possible therefore to suggest that they have accurately modelled language but not modelled meaning. Because they don't have meaning modelled they are not "grounded" in either definitions or experience whereas humans are.

BUT... a counter argument is that the prompt is the grounding (which is why prompt engineering "works") and RHLF is the "experience". You can maybe take both of these together and have the situation that real soon now (less than 5 years) we have a sufficiently well trained LLM via RHLF that it responds just like a human would. Maybe... ha.

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