341–350 of 396 posts
Maybe we could have a smaller LLM just for translating caveman back into redditor?
I cannot wait for this to become the normal and expected way to interact with LLMs in the coming decades as humanity reaches the limit of compute capacity. Why waste 3/4th? Maybe we could have a smaller LLM just for translating caveman back into redditor?
Now I full caveman.
There's linguistic term for this kind of speech: isolating grammars, which don't decline words and use high context and the bare minimum of words to get the meaning across. Chinese is such a language btw. Don't know what Chinese think about their language being regarded as cavemen language...
There's linguistic term for this kind of speech: isolating grammars, which don't decline words and use high context and the bare minimum of words to get the meaning across. Chinese is such a language btw. Don't know what Chinese think about their language being regarded as cavemen language...
English is diarrhea mouth language. Which is worse?
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In the regular people forums (twitter, reddit), you see endless complaints about LLMs being stupid and useless. But you also catch a glimpse of how the author of the complaint communicates in general... "im trying to get the ai to help with the work i am doing to give me good advice for a nice path to heloing out and anytim i askin it for help with doing this it's total trash i dunt kno what to do anymore with this d…
The realization is LLMs are computer programs. You orchestrate them like any other program and you get results. Everyone's interfaces, concept and desires are different so the performance is wildly varied This is similar to frameworks: they were either godsends or curses depending on how you thought and what you were doing ..
Basically treat the LLM as a human. Not as a computer. Like a junior developer or an intern (for the most part).
That said you need to know what to ask for and how to drive the LLM in the correct direction. If you don't know anything you're likely not going to get there.
Cute idea, but you're never gonna blow your token budget on output. Input tokens are the bottleneck, because the agent's ingesting swathes of skills, directory trees, code files, tool outputs, etc. The output is generally a few hundred lines of code and a bit of natural language explanation.
In single-turn use, yeah, but across dozens of turns there's probably value in optimizing the output. Btw your point lands just as well without "Cute idea, but" https://odap.knrdd.com/patterns/condescending-reveal
Like your site and good luck with improving discourse on the Internet.
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> There is actual research suggesting concise prompting can reduce response length substantially without always wrecking quality, Anecdote: i discussed that with an LLM once and it explained to me that LLMs tend to respond to terse questions with terse answers because that's what humans (i.e. their training data) tend to do. Similarly, it explained to me that polite requests tend to lead to LLM responses with _more_…
LLMs don't understand what they are doing, they can't explain it to you, it's just creating a reasonable sounding response
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But that response is grounded in the training data they've seen, so it's not entirely unreasonable to think their answer might provide actual insights, not just statistical parroting.
What do you mean? It is grounded on the text it is fed, the reason it said that was that humans have said that or something similar to it, not because it analyzed a lot of LLM information and thought up that answer itself. LLM can "think" but that requires a lot of tokens to do, all quick answers are just human answers or answers it was fed with some basic pattern matching / interpolation.
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LLMs architectures have not changed at all since 2023. > but mmuh latest SOTA from CloudCorp (c)! You don't know how these things work and all you have to go on is marketing copy.
Yea you don't know anything about LLM architectures. They often change with each model release. You also aren't aware that there's more to it than "LLM architecture". And you're rather confident despite your lack of knowledge. You're like the old LLMs before ChatGPT was released that were kinda neat, but usually wrong and overconfident about it.
The only new innovation is MoE, something that's used to optimize local models and not for the "SOTA" cloud offerings you're so fond of.
This is neat but my employer rates my performance based on token consumption; is there one that makes Claude needlessly verbose?
Is this a joke, or are you serious? Do you work for Nvidia?