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Some of us really value offline and uncensored LLMs for this and more reasons, but that doesn’t solve the problem it just reduces or changes the bias.
by SV_BubbleTime 1y ago
Some of us really value offline and uncensored LLMs for this and more reasons, but that doesn’t solve the problem it just reduces or changes the bias.
- heavyset_go 1y agoAs long as we have to rely on pre trained networks and curated training sets, normal people will not be able to surpass this issue.
- ghxst 1y agoIf the training data was "censored" by leaving out certain information, is there any practical way to inject that missing data after the model has already been trained?
- heavyset_go 1y agoYou can fine tune a model with new information, but it is not the same thing as training it from scratch, and can only get you so far. You might even be able to poison a model against being fine-tuned on certain information, but that's just a conjecture.
- calaphos 1y agoIf it's just filtered out in the training sets, adding the information as context should work out fine - after all this is exactly how o3, Gemini 2.5 and co deal with information that is newer than their training data cutoff.
- selfhoster11 1y agoYes, RAG is one way to do that.