Or, you might save token sampling telemetry (perplexity, etc) alongside a CoT and result. So when read, it's like a captured performance - this sentence smells "hesitant", that one "confused". Poetry vs prose. Or, a consistentcy checker might add smells of "something's not right here". Or... emojis that emote.
For a dog, that's not merely a lamppost, it's richly-evocotive local history. To a dev long experienced with some codebase, that's not merely a filename, it's that nasty file that bites.
One open question is whether you can find and calibrate embeddings to provide an informative whiff, without badly degrading reasoning. And be cautious of, and suspicious of changes to, a scary file, without becoming too avoidant. Also, salience bias. Also, imagine debugging scent hallucinations.
Activation-rich text - auxiliary non-linguistic embeddings as meta-signals... the random silliness local LLMs encourage.
Like, if someone mistakes a manikin or scarecrow for an innocent person, and takes action in an attempt to harm that imagined person (e.g. they try to mug the imagined person), they’ve still done something wrong, even though the person they intended to wrong never actually existed.
I guess maybe it kind of depends how strongly and deeply one feels as if the manikin/scarecrow/chatbot is a person? If one is playing make believe using scarecrow, role playing as a mugger, but only as a game, then that’s probably fine I guess. Like, I don’t want to say that it is immoral to play an evil character in a D&D campaign; I don’t think that’s true.
But if one is messing with some ants, and one conceives of oneself as “torturing some ants”, I think one is fairly likely doing something wrong even though I don’t think the ants have a well-being, and there’s nothing wrong with killing a bunch of ants. And I think this is still true even if one has the belief “ants don’t actually have a well-being” at the same time as one conceives of what one is doing as “torturing some ants”.
Yelling at your Ai will trigger the weights which are around yelling in the training data, which is more often than not... not the areas you want to be activating.