Just an example: in its final years, Radio Shack fired most of its more experienced (higher paid) employees and kept/hired younger employees for less pay.

The real question to ask is, does it contain any chemicals?

I only eat ham that is 100% composed of virtual particles.

When the shopkeeper thinks he has the concept of a deal.

OneDrive: the best idea since sliced bread.

I don’t like the idea of restricting the model’s corpus further. Rather, I think it would be good if it used a bigger corpus, but added the date of origin for each element as further context.

Separately, I think it could be good to train another LLM to recognize biases in various content, and then use that to add further context for the main LLM when it ingests that content. I’m not sure how to avoid bias in that second LLM, though. Maybe complete lack of bias is an unattainable ideal that you can only approach without ever reaching it.

How about a hash brown?

Would anyone want any toast?

Wouldn’t be rare for someone in Québec to ask “Comment tu t’appelles?” but it’s generally understood that this is “colloquial” French, whereas “Comment t’appelles-tu?” is the correct way to say and write it in French.

It’s generally better to learn the “correct” language before you start applying colloquialisms.