Comment by tarsinge
15 hours ago
The reasoning is by being polite the LLM is more likely to stay on a professional path: at its core a LLM try to make your prompt coherent with its training set, and a polite prompt + its answer will score higher (gives better result) than a prompt that is out of place with the answer. I understand to some people it could feel like anthropomorphising and could turn them off but to me it's purely about engineering.
Edit: wording
> The reasoning is by being polite the LLM is more likely to stay on a professional path
So no evidence.
> If the result of your prompt + its answer it's more likely to score higher i.e. gives better result that a prompt that feels out of place with the answer
Sure seems like this could be the case with the structure of the prompt, but what about capitalizing the first letter of sentence, or adding commas, tag questions etc? They seem like semantics that will not play any role at the end
Writing is what gives my thinking structure. Sloppy writing feels to me like sloppy thinking. My fingers capitalize the first letter of words, proper nouns and adjectives, and add punctuation without me consciously asking them to do so.
Why wouldn't capitalization, commas, etc do well?
These are text completion engines.
Punctuation and capitalization is found in polite discussion and textbooks, and so you'd expect those tokens to ever so slightly push the model in that direction.
Lack of capitalization pushes towards text messages and irc perhaps.
We cannot reason about these things in the same way we can reason about using search engines, these things are truly ridiculous black boxes.
> Lack of capitalization pushes towards text messages and irc perhaps.
Might very well be the case, I wonder if there's some actual research on this by people that have some access to the the internals of these black boxes.
That's orthography, not semantics, but it's still part of the professional style steering the model on the "professional path" as GP put it.
For me it is just a good habit that I want to keep.
I remember studies that showed that being mean with the LLM got better answers, but by the other hand I also remember an study showing that maximizing bug-related parameters ended up with meaner/malignant LLMs.
Surely this could depend on the model, and I'm only hypothesizing here, but being mean (or just having a dry tone) might equal a "cut the glazing" implicit instruction to the model, which would help I guess.