You’re not wrong, and it’s something we “doomers” have been saying since OpenAI dumped ChatGPT onto folks. These are curated walled gardens, and everyone should absolutely be asking what ulterior motives are in play for the owners of said products.

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.

As long as we have to rely on pre trained networks and curated training sets, normal people will not be able to surpass this issue.

If 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?

If 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.

You 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.

Yes, RAG is one way to do that.

"Filtered" in which way?