Yes, with sufficient context, that's what I do every day, as presentation authors, textbook authors and Internet commentariat alike, all keep making typos and grammar errors.
You can't deal with humans without constantly trying to guess what they mean and use it to error-correct what they say.
(This is a big part of what makes LLMs work so well on wide variety of tasks, where previous NLP attempts failed.)
I often wish LLMs would tell me outright the assumptions they make on what I mean. For example, if I accidentally put “write an essay on reworkable energy”, it should start by saying “I'm gonna assume you mean renewable energy”. It greatly upsets me that I can't get it to do that just because other people who are not me seem to find that response rude for reasons I can't fathom, so it was heavily trained out of the model.
Huh, I'd expect it do exactly what you want it to, or some equivalent of it. I've never noticed LLMs silently make assumptions on what I meant wrt. anything remotely significant; they do stellar job at being oblivious to typos, bad grammar and other fuckups of ESL people like me, and (thankfully) they don't comment on that, but otherwise, they've always been restating my requests and highlighting if they're deviating from direct/literal understanding.
Case in point, I recently had ChatGPT point out, mid-conversation, that I'm incorrectly using "disposable income" to mean "discretionary income", and correctly state this must be the source of my confusion. It did not guess that from my initial prompt; it took my "wrong figures" at face value and produced answers that I countered with some reasoning of my own; only then, it explicitly stated that I'm using the wrong term because what I'm saying is correct/reasonable if I used "discretionary" in place of "disposable", and proceeded to address both versions.
IDK, but one mistake I see people keep making even today, is telling the models to be succinct, concise, or otherwise minimize the length of their answer. For LLMs, that directly cuts into their "compute budget", making them dumber. Incidentally, that could be also why one would see the model make more assumptions silently - these are one of the first things to go when one's trying to write concisely. "Reasoning" models are more resistant to this, fortunately, as the space between the "<thinking> tags" is literally the "fuck off user, this is my scratchpad, I shall be as verbose as I like" space, so one can get their succinct answers without compromising the model too badly.
You can't deal with humans without constantly trying to guess what they mean and use it to error-correct what they say.
(This is a big part of what makes LLMs work so well on wide variety of tasks, where previous NLP attempts failed.)