With the ‘autocomplete-like’ nature of current LLMs the issue is precisely that you can never be sure of any answer’s validity. Some approaches try by giving ‘sources’ next to it, but that doesn’t mean those sources’ findings actually match the text output and it’s not a given that the sources themselves are reputable - thus you’re back to perusing those to make sure anyway.
If there was a meter of certainty next to the answers this would be much more meaningful for serious use-cases, but of course by design such a thing seems impossible to implement with the current approaches.
I will say that in my personal (hobby) projects I have found a few good use cases of letting the models spit out some guesses, e.g. for the causes of a programming bug or proposing directions to research in, but I am just not sold that the heaviness of all the costs (cognitive, social, and of course environmental) is worth it for that alone.
I think you really nailed the crux of the matter.
With the ‘autocomplete-like’ nature of current LLMs the issue is precisely that you can never be sure of any answer’s validity. Some approaches try by giving ‘sources’ next to it, but that doesn’t mean those sources’ findings actually match the text output and it’s not a given that the sources themselves are reputable - thus you’re back to perusing those to make sure anyway.
If there was a meter of certainty next to the answers this would be much more meaningful for serious use-cases, but of course by design such a thing seems impossible to implement with the current approaches.
I will say that in my personal (hobby) projects I have found a few good use cases of letting the models spit out some guesses, e.g. for the causes of a programming bug or proposing directions to research in, but I am just not sold that the heaviness of all the costs (cognitive, social, and of course environmental) is worth it for that alone.