121–130 of 156 posts
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That's just additional context.
That the person go and get themselves. If a model could to that we wouldn't need you to drive them. Basically every human is self going that way, you don't need to go and pick them up since they got stuck in a loop of unknowns at a grocery store etc.
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Simple maths problems are simple logic problem. Here it doesn't even have to come up with a reasoning, it probably already memorised how to solve sums. Yet it fails at that, it shows it cannot solve logic problems if there are too much steps. > All indications are that it will continue to become smarter. I'm not disputing that, every new model score better at my benchmark, but right now, none truly "solve" one of the…
If it can frame the question for the tool, it therefore has the logic (whether that was static recall or deductive). LLM's struggle with simple maths by nature of their architecture not due to a lack of logic. Yes it struggles with logic questions too but they're not directly related here.
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Nit pick, but proof by contradiction is necessarily stronger as it is deductive reasoning, and this kind of "proof" by anecdotal evidence doesn't rise above abductive reasoning. Still useful, very much not a proof.
We don't have a formal model of how/why any given LLM works, and incidentally we're also short on proofs for real-world software and organizations. Empirical facts are the strongest thing we have in this domain.
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> LLM's struggle with simple maths by nature of their architecture not due to a lack of logic. No, if it was good at logic it would have overcame that tiny architectural hurdle, its such a trivial process to convert tokens to numbers that it is ridiculous for you to suggest that is the reason it fails at math. The reason it fails at math is because it fails at logic, and math is the most direct set of logic we have.…
It can't see that data so how can it convert it? It can only see the token input. An analogy (probably poor) is like asking a human to see UV light. We can do so but only with tools or by removing our lense. The fact that SOTA models (not yet publicly available) can achieve gold at IOM implies otherwise.
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We will never have a post scarcity society. Automation can make certain foodstuffs and manufactured goods somewhat cheaper but the things that people really want will always be in short supply, for example real estate in geographically favorable areas.
When the celibate classes have been able to sublimate what is arguably the strongest of all wants for as long as they have, I doubt there is any desire that could not be redirected with similar techniques.
I very much disagree. To attempt a proof by contradiction: Let us assume that the author's premise is correct, and LLMs are plenty powerful given the right context. Can an LLM recognize the context deficit and frame the right questions to ask? They can not: LLMs have no ability to understand when to stop and ask for directions. They routinely produce contradictions, fail simple tasks like counting the letters in a wo…
It's all about tools. Given sufficient tooling, the model's inherent abilities become irrelevant. Give a model a tool that counts characters and it will get this question right 100% of the time. Copy and paste to your domain. And what are tools but a means of providing context from the real world? People seem blinded by focusing on the raw abilities of models, missing the fact that these things should be seen simply as reasoning engines for tool usage.
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if an llm is unreliable, then why would another just-as-unreliable llm make it any better?
If a hard drive sometimes fails, why would a raid with multiple hard drives be any more reliable? "Do task x" and "Is this answer to task x correct?" are two very different prompts and aren't guaranteed to have the same failure modes. They might, but they might not.
This is not quite the same situation. It's also the core conceit of self-healing file systems like ZFS. In the case of ZFS it not only stores redundant data but redundant error correction. It allows failures to not only be detected but corrected based on the ground truth (the original data).
In the case of an LLM backstopping an LLM, they both have similar probabilities for errors and no inherent ground truth. They don't necessarily memorize facts in their training data. Even with a RAG the embeddings still aren't memorized.
It gives you a constant probability for uncorrectable bullshit. One of the biggest problems with LLMs is the opportunity for subtle bullshit. People can also introduce subtle errors recalling things but they can be held accountable when that happens. An LLM might be correct nine out of ten times with the same context or only incorrect given a particular context. Even two releases of the same model might not introduce the error the same way. People can even prompt a model to error in a particular way.
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I truly love this comment, which essentially says: LLMs are glorified calculators, with ambiguous grammar. :)
Computers are glorified calculators, yet they power most of our lives
WolframAlpha is a more impressive front end to a calculator than I've seen out of LLMs. Not only does it show me how it translated my natural-ish language query but it shows me potential alternative interpretations to my question. LLMs by the nature of how training works can't necessarily tell me why and how they interpreted my prompt. The thinking models are better but still not great.
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I'm not the fellow you replied to, but I felt like stepping in. > That’s interesting, you added a tool. The "tool" in this case, is a memory aid. Because they are computer programs running inside a fairly-ordinary computer, the LLMs have exactly the same sort of tool available to them. I would find a claim that LLMs don't have a free MB or so of RAM to use as scratch space for long addition to be unbelievable.
The fact that an LLM is running inside an ordinary computer does not mean that it gets to use all the abilities of that computer. They do not have megabytes of scratch space merely because the computer has a lot of memory. They do have something a bit like it: their "context window", the amount of input and recently-generated output they get to look at while generating the next token. Claude Sonnet 4 has 1M tokens of…
I very much disagree. To attempt a proof by contradiction: Let us assume that the author's premise is correct, and LLMs are plenty powerful given the right context. Can an LLM recognize the context deficit and frame the right questions to ask? They can not: LLMs have no ability to understand when to stop and ask for directions. They routinely produce contradictions, fail simple tasks like counting the letters in a wo…
This is not a proof by contradiction - you have stated an assumption followed by a bunch of non-sequitors about what LLMs can and can't do, also known as begging the question. Under the conditions of your assumption (namely that LLMs are plenty powerful with the right context) why would you believe anything in your last paragraph? That's how a proof by contradiction works. (not saying you are wrong, necessarily, but…
I don't think I stated an assumption, this is an assertion, worded rhetorically. You are welcome to disagree with it and refute it, but its structural role is not that of an assumption.
"Can an LLM recognize the context deficit and frame the right questions to ask?"
> a bunch of non-sequitors
I'm guessing you're referring to the "canvas or not" bit? The sequitir there was that LLMs routinely fail to execute simple instructions for which they have all the context.
> not saying you are wrong
Happy to hear counterarguments of course, but I do not yet see an argument for why what I said was not structurally coherent as counterexamples, nor anything that weakens the specifics of what I said.