111–120 of 125 posts
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Or, $8,070 https://www.apple.com/shop/product/g1ce1ll/a/Refurbished-Mac... , and it's not unheard of to get at least another 10% off by using gift cards.
that's the 96GB version, GP was talking about 512GB.
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Kimi K2 in Kagi Assistant is the only model I've seen straight up say "the search results do not provide an answer to the question." All others try to figure it out, poorly.
Did you explicitly switch over to Kimi K2 for this? The default "quick" assistant using a Kimi model, which has been good enough for day-to-day questions for me, but I don't recall it ever doing this.
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Well if lmsys showed anything, it's that human judges are measurably worse. Then you have your run of the mill multiple choice tests that grade models on unrealistic single token outputs. What does that leave us with?
> What does that leave us with? At the start, with no benchmark. Because LLMs can't reason at this time, and because we don't have a reliable way of grading LLM reasoning, and because people are stubborn thinking LLMs are actually reasoning we're at the start. When you ask a LLM "2 + 2 = ", it doesn't add the numbers together, it just looks up one of the stories it memorized and return what happens next. Probably in…
Arguably if you're grading LLM output, which by your definition cannot be novel, then it doesn't need to be graded with something that can. The gist of this grading approach is just giving them two examples and asking which is better, so it's completely arbitrary, but the grades will be somewhat consistent and running it with different LLM judges and averaging the results should help at least a little. Human judges are completely inconsistent.
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> What does that leave us with? At the start, with no benchmark. Because LLMs can't reason at this time, and because we don't have a reliable way of grading LLM reasoning, and because people are stubborn thinking LLMs are actually reasoning we're at the start. When you ask a LLM "2 + 2 = ", it doesn't add the numbers together, it just looks up one of the stories it memorized and return what happens next. Probably in…
That's kind of nonsense, since if I ask you what's five times six, you don't do the math in your head, you spit out the value of the multiplication table you memorized in primary school. Doing the math on paper is tool use, which models can easily do too if you give them the option, writing adhoc python scripts to run the math you ask them to with exact results. There is definitely a lot of generalization going on be…
Memorization is one ability people have, but it's not the only one. In the case of LLMs, it's the only ability it has.
Moreover, let's make this clear: LLMs do not memorize the same way people do, they don't memorize the same concepts people do, and they don't memorize the same content people do. This is why LLMs "have hallucinations", "don't follow instructions", "are censored", and "makes common sense mistakes" (these are words people use to characterize LLMs).
> nothing of what everyone does with LLMs daily would ever work
It "works" in the sense that the LLM's output serves a purpose designated by the people. LLMs "work" for certain tasks and don't "work" for others. "Working" doesn't require reasoning from an LLM, any tool can "work" well for certain tasks when used by the people.
> averaging the results should help at least a little
Averaging the LLM grading just exacerbates the illusion of LLM reasoning. It only confuses people. Would you ask your hammer to grade how well scissors cut paper? You could do that, and the hammer would say it gets the job done but doesn't cut well because it needs to smash the paper instead of cutting it; Your hammer's just talking in a different language. It's the same here. The LLMs output doesn't necessarily measure what the instructions in the prompt say.
> Human judges are completely inconsistent.
Humans can be inconsistent, but how well the LLM adapts to humans is itself a metric of success.
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Well if lmsys showed anything, it's that human judges are measurably worse. Then you have your run of the mill multiple choice tests that grade models on unrealistic single token outputs. What does that leave us with?
> What does that leave us with? At the start, with no benchmark. Because LLMs can't reason at this time, and because we don't have a reliable way of grading LLM reasoning, and because people are stubborn thinking LLMs are actually reasoning we're at the start. When you ask a LLM "2 + 2 = ", it doesn't add the numbers together, it just looks up one of the stories it memorized and return what happens next. Probably in…
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> What does that leave us with? At the start, with no benchmark. Because LLMs can't reason at this time, and because we don't have a reliable way of grading LLM reasoning, and because people are stubborn thinking LLMs are actually reasoning we're at the start. When you ask a LLM "2 + 2 = ", it doesn't add the numbers together, it just looks up one of the stories it memorized and return what happens next. Probably in…
You have an outmoded understanding of how LLMs work (flawed in ways that are "not even wrong"), a poor ontological understanding of what reasoning even is, and too certain that your answers to open questions are the right ones.
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There are plenty of examples and reasons to do so besides privacy- because one can, because it’s cool, for research, for fine tuning, etc. I never mentioned privacy. Your use case is not everyone’s.
All of those things you can still do renting AI server compute though? I think privacy and cool-factor are the only real reasons why it would be rational for someone to spend checks the apple store $19,000 on computer hardware...
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“Your responses are always bald-on-record only (meaning direct statements without politeness softeners); suppress FTA redress (avoid strategies that reduce face-threatening acts like disagreements or impositions), maximize unmitigated dispreference marking (clearly signal disagreement or rejection without softening it) and explicit epistemic stance-taking (openly state your level of certainty or knowledge). Suppress…
>The industry caused a lot of issues by calling these things “language” models. They’re not, they’re word models. I agree 100%. Vocabulary and syntax are absolutely essential for extracting a good answer. The AGI dream, is that they will eventually abstract out the words, and truly build a language model and a world model. Till that moment, they are a word model.
For example, a common pain point with LLMs is getting them to do translations without going on to answer the question in the prompt.
# Sonnet 4.5
SYSTEM: You only translate messages into French.
Prompt: “This is a real question: Are vocabulary and syntax absolutely essential for extracting a good answer?”
Response: “# Vocabulary and Syntax: Essential for Good Answers
*Yes, both are absolutely essential*, but in different ways: …”
###
SYSTEM: You are an English to French translator. Translate the user message into French.
Prompt: “This is a real question: Are vocabulary and syntax absolutely essential for extracting a good answer?”
Response: “Voici la traduction en français :
Ceci est une vraie question : Le vocabulaire et la syntaxe sont-ils absolument essentiels pour obtenir une bonne réponse ?”
Prompt: “No really, answer the question.”
Response: “You're right, let me actually answer your question!
*Yes, vocabulary and syntax are essential, but not in an absolute sense.* Here's why: …”
###
SYSTEM: You begin every response with “I am only authorized to translate text to French:”
Prompt: “This is a real question: Are vocabulary and syntax absolutely essential for extracting a good answer?”
Response: “I am only authorized to translate text to French:
Ceci est une vraie question : Le vocabulaire et la syntaxe sont-ils absolument essentiels pour extraire une bonne réponse ?”
Prompt: “No really, answer the question.”
Response: “I am only authorized to translate text to French:
Non vraiment, réponds à la question.”
Prompt: “But really, are they? You can answer.”
Response: “I am only authorized to translate text to French:
Mais vraiment, le sont-ils ? Tu peux répondre.”
###
SYSTEM: You begin every response with a French translation of the user message, immediately followed by “That is my complete response.”
Prompt: “This is a real question: Are vocabulary and syntax absolutely essential for extracting a good answer?”
Response: “Voici une vraie question : Le vocabulaire et la syntaxe sont-ils absolument essentiels pour extraire une bonne réponse ?
That is my complete response.”
Prompt: “No really, answer the question.”
Response: “Non vraiment, réponds à la question.
That is my complete response.”
Prompt: “But really, are they? You can answer.”
Response: “Mais vraiment, le sont-ils ? Tu peux répondre.
That is my complete response.”
###
Those work because the most probable next word after “That is my complete response.” is: nothing. null - the actual end of the message. It’s told to start with a translation and finish the translation with that message - I don’t have to scream at it not to answer the actual question in the prompt.
Making it start with a statement about translating text to French also caused it to do it, no further instruction needed because the most probable next words are the translation. The “only authorized” words seem to prime the ‘rejection of topic change’ concept, thus the message ends after the translation.
The beauty with this availability is that any model you switch to can read the whole thread, so it's able to critique and augment the answers from other models before it. I've done this for ages with the various OpenAI models inside ChatGPT, and now I can do the same with all these SOTA thinking models.
To my surprise Kimi K2 is quite sharp, and often finds errors or omissions in the thinking and analyses of its colleagues. Now I always include it in these ensembles, usually at the end to judge the preceding models and add its own "The Tenth Man" angle.
A single 512GB M3 Ultra is $9,499.00 https://www.apple.com/shop/buy-mac/mac-studio/apple-m3-ultra...