Comment by ethbr1
11 hours ago
Using LLMs well with human language requires precision.
There are only a few professions that are intrinsically precise with language.
No surprise we're seeing adoption in software programming and mathematics.
11 hours ago
Using LLMs well with human language requires precision.
There are only a few professions that are intrinsically precise with language.
No surprise we're seeing adoption in software programming and mathematics.
If you work with spec files maybe.
For regular work, since we use modern reasoning models, you might as well type your requests with the wrong keyboard layout selected.
It still decodes your intent and completes the requested task. You can work from your phone with voice dictation. It requires no precision at all.
You are essentialy interacting with a SaaS that can be modified and altered at any point by its creator. Treating the LLM as some predictable entity that always works orders of magnitude better with precise language needs to be supported by evidence for each specific case.
Since the vast number of potential users of LLMs are, as you say, poor practitioners of precise language, it is incumbent on LLM providers to continuously iterate on providing LLMs that work better with imprecise language.
Hence, the people who claim that precise language is some 10x - 100x productivty skill seem to ignore the cycle of big LLM providers who are optimizing the models for users who lack those. (One of the early optimizations was the chatbot asking follow-up questions to the user for example).
So I think the people who think that their precise language skills are going to allow them to avoid the jobocalypse are also in a separate bubble of theit own. Precise language skills are going to matter less and less as LLMs increase their hand-holding skills for less equipped users.
This presupposes that the llm will be able to pick up the slack for the users of imprecise language as to me that is a proxy for domain expertise. If so then the human is not needed which does seem like a stretch.
I think the argument that precise language doesn't matter is even harder than that.
It requires that LLMs are equally effective with imprecise language as precise language.
And while I'm sure the frontier labs are continually iterating on 'garbage in, something vaguely useful out', it's a high bar to get that output to be as useful as if it were more precisely prompted.
My view is that the human is needed for verifying the source of truth, not for providing precise language which the LLM is continuously trained to adapt better without.
I have not come across a rigorously argued technical analysis that has claimed that using precise language can in any way eliminate the possibility of hallucinations or unaligned behavior. Short of providing a proof for the elimination of hallucinations and confabulations, merely reducing the behavior by a random percentage is not an acceptable status quo for completely removing the human.