Caveman: Why use many token when few token do trick
1–10 of 396 posts
Re: Caveman: Why use many token when few token do trick
#2[deleted]
Re: Caveman: Why use many token when few token do trick
#3No articles, no pleasantries, and no hedging. He has combined the best of Slavic and Germanic culture into one :)
Re: Caveman: Why use many token when few token do trick
#4While really useful now, I'm afraid that in the long run it might accelerate the language atrophy that is already happening. I still remember that people used to enter full questions in Google and write SMS with capital letters, commas and periods.
Re: Caveman: Why use many token when few token do trick
#5Oh boy. Someone didn't get the memo that for LLMs, tokens are units of thinking. I.e. whatever feat of computation needs to happen to produce results you seek, it needs to fit in the tokens the LLM produces. Being a finite system, there's only so much computation the LLM internal structure can do per token, so the more you force the model to be concise, the more difficult the task becomes for it - worst case, you can guarantee not to get a good answer because it requires more computation than possible with the tokens produced.
I.e. by demanding the model to be concise, you're literally making it dumber.
(Separating out "chain of thought" into "thinking mode" and removing user control over it definitely helped with this problem.)
Re: Caveman: Why use many token when few token do trick
#6So it's a prompt to turn Jarvis into Hulk!
Re: Caveman: Why use many token when few token do trick
#7You can also make huge spelling mistakes and use incomplete words with llms they just sem to know better than any spl chk wht you mean. I use such speak to cut my time spent typing to them.
Re: Caveman: Why use many token when few token do trick
#8Oh boy. Someone didn't get the memo that for LLMs, tokens are units of thinking . I.e. whatever feat of computation needs to happen to produce results you seek, it needs to fit in the tokens the LLM produces. Being a finite system, there's only so much computation the LLM internal structure can do per token, so the more you force the model to be concise, the more difficult the task becomes for it - worst case, you ca…
I remember a while back they found that replacing reasoning tokens with placeholders ("....") also boosted results on benchies.
But does talk like caveman make number go down? Less token = less think?
I also wondered, due to the way LLMs work, if I ask AI a question using fancy language, does that make it pattern match to scientific literature, and therefore increase the probability that the output will be true?
Re: Caveman: Why use many token when few token do trick
#9Wouldn't this affect quality of output negatively?
Thanks to chain of thought, actually having the LLM be explicit in its output allows it to have more quality.
Re: Caveman: Why use many token when few token do trick
#10Idk I try talk like cavemen to claude. Claude seems answer less good. We have more misunderstandings. Feel like sometimes need more words in total to explain previous instructions. Also less context is more damage if typo. Who agrees? Could be just feeling I have. I often ad fluff. Feels like better result from LLM. Me think LLM also get less thinking and less info from own previous replies if talk like caveman.