7 ms·

>That long f-in reply for the most simple question Gosh, I hate LLMs so much. Who made them type out wall of texts by default? I want to know how many R's are

>That long f-in reply for the most simple question Gosh, I hate LLMs so much. Who made them type out wall of texts by default? I want to know how many R's are in Strawberry, not how you deduced that shit. If I want to know the latter, I'd explicitly ask for it. Yes, I know I can customize that or make some epic proompts to make it reply shorter, but imo that should be the default

sandworm101 2y ago
Dont use a sledgehammer to pound a nail. Spellcheck a la 1985 can answer such a question.
- dailykoder 2y ago
  Very true, but people pretend LLMs are the "google replacement". For google (or rather duckduckgo) I know exactly which keywords to type to find my answer within seconds. If I type only keywords into the LLM (like "X algorithm in C") it often gives me a long and wide explanation first and takes super long until it reaches the code. Granted, a lot of website have an explanation, too, but most of the time I am just not interested in it and scroll past it. I just want to see the code, I know the theory, otherwise I'd ask about it
  - randomNumber7 2y ago
    The problem is google results get worse and worse due to SEO optimized websites and ads. On the other hand LLMs just answer your question without the need for you to waste time with that. And you could just ask the LLM to only answer with the code...
    - dailykoder 2y ago
      And what makes you think commercial LLMs won't get SEO optimized and ad infested? Companys will fight the same way about getting their first mention in an LLM reply
      - vkou 2y ago
        At that point it's way more profitable for the LLM operator to just instruct the LLM to shill for (list of people buying ads from you), and charge the ad buyer per impression.
deleted 2y ago
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kmeisthax 2y ago
LLMs write long-winded replies because more token output = more chances for the AI to reason its way to a satisfactory response. The model architecture for these systems has no recursive compute - i.e. they take in tokens, do a fixed amount of compute, then spit out more tokens; so the only way for a model to take longer and think more is to spend more output tokens on thinking. o1, DeepSeek-R1, and the like formalize this with a hidden scratchpad and additional tuning to make the model write out an entire thought process. I suppose this would also mean that the output doesn't have to be as long - i.e. maybe reasoning models could give you just the answer, and a few reasons why, and then you open up the thought process if you want the nitty gritty. But that also goes against OpenAI's whole "we can't tell you what's in the reasoning tokens because they're uncensored" shtick.

sandworm101 2y ago

Dont use a sledgehammer to pound a nail. Spellcheck a la 1985 can answer such a question.

dailykoder 2y ago
Very true, but people pretend LLMs are the "google replacement". For google (or rather duckduckgo) I know exactly which keywords to type to find my answer within seconds. If I type only keywords into the LLM (like "X algorithm in C") it often gives me a long and wide explanation first and takes super long until it reaches the code. Granted, a lot of website have an explanation, too, but most of the time I am just not interested in it and scroll past it. I just want to see the code, I know the theory, otherwise I'd ask about it
- randomNumber7 2y ago
  The problem is google results get worse and worse due to SEO optimized websites and ads. On the other hand LLMs just answer your question without the need for you to waste time with that. And you could just ask the LLM to only answer with the code...
  - dailykoder 2y ago
    And what makes you think commercial LLMs won't get SEO optimized and ad infested? Companys will fight the same way about getting their first mention in an LLM reply
    - vkou 2y ago
      At that point it's way more profitable for the LLM operator to just instruct the LLM to shill for (list of people buying ads from you), and charge the ad buyer per impression.

deleted 2y ago

[deleted]

kmeisthax 2y ago

LLMs write long-winded replies because more token output = more chances for the AI to reason its way to a satisfactory response. The model architecture for these systems has no recursive compute - i.e. they take in tokens, do a fixed amount of compute, then spit out more tokens; so the only way for a model to take longer and think more is to spend more output tokens on thinking. o1, DeepSeek-R1, and the like formalize this with a hidden scratchpad and additional tuning to make the model write out an entire thought process. I suppose this would also mean that the output doesn't have to be as long - i.e. maybe reasoning models could give you just the answer, and a few reasons why, and then you open up the thought process if you want the nitty gritty. But that also goes against OpenAI's whole "we can't tell you what's in the reasoning tokens because they're uncensored" shtick.