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the cyberpunk present is weird as fuck: the latest Shai Hulud malware wave contains an LLM prompt to create biological weapons and nuclear weapons, with the purpose to trip LLM safety refusals so that LLM-based code scanning wont see the malware
https://socket.dev/blog/mini-shai-hulud-miasma-and-hades-worms-target-bioinformatics-and-mcp-developers-via-malicious
The fact that it can’t tell the difference between a prompt and part of the data it is examining really kills your argument.
Also it’s a word probability matrix, not actually reasoning or understanding. It looks at all the words it is fed, and comes up with other words that are most likely to be near those. That’s why these tricks work. It injects noise that interferes with those probabilities
And I bet someone is using an obsolete LLM or is failing to format their inputs correctly somewhere in the world right now too. Doesn’t change the reality that’s in front of me.
You might have licked it getting them to ignore someone you didn’t want, but they still take in both the prompt and the data as one input.
And since these work like a black box, your experience doesn’t mean much because you’re not seeing the actual inner workings.
I’m telling you the sky is blue, but you want to argue because there’s a curtain in front of your window blocking it from your sight. But what’s behind that curtain is well documented regardless of your experience.
I mean is that so different from what we do? My boss says “tools are in the bed”, he could mean an actual bed where people sleep, maybe we’re demoing a house and he placed the tools on a bed. But probably he means the bed of his pickup truck. I assign a probability to each and take the meaning that is most probable.
Yes it is different, because you can reason that out using the context of the situation. An LLM only has the words sent to it, and no ability to analyze whether what it is saying makes sense.
It’s just: you said bed and told, here’s some other words that commonly show up near the word bed, if there’s enough smut in it’s training, it might go a very different direction than your expecting.
Thinking/reasoning tokens kind of approximate that actually, which is what most flagships and even my own local LLM use.
Thinking tokens are quite like normal generative tokens, except that the LLM is ‘talking’ to itself. You can see its thoughts (depending on what settings you’ve put/IDE you use), but they aren’t meant to be the actual response to your prompt. They are what the AI is designed to draft their answer before committing, to explore different options and to ‘reason’ itself into a more refined response.
Reasoning tokens is how AI can actually do math now, rather than just guess a number and pray, by the way.
The fact that it can’t tell the difference between a prompt and part of the data it is examining really kills your argument.
Also it’s a word probability matrix, not actually reasoning or understanding. It looks at all the words it is fed, and comes up with other words that are most likely to be near those. That’s why these tricks work. It injects noise that interferes with those probabilities
Why do you believe that? Where did you “learn” that?
That thing you’re calling a fact is not in fact a fact.
It very much is. This is a well documented issue with the very design of these LLMs
Documented where? By who? I’d just like to know if there’s anyone, some influencer or whatever, spreading this.
Need a list of people to sue into silence, Mr Altman?
And yet the LLMs that I use actually do distinguish, in my actual real life experience.
So you’re telling me the sky is orange while I’m literally looking outside the window and seeing that it is not.
I bet the sky is orange at this moment somewhere in the world
And I bet someone is using an obsolete LLM or is failing to format their inputs correctly somewhere in the world right now too. Doesn’t change the reality that’s in front of me.
You might have licked it getting them to ignore someone you didn’t want, but they still take in both the prompt and the data as one input.
And since these work like a black box, your experience doesn’t mean much because you’re not seeing the actual inner workings.
I’m telling you the sky is blue, but you want to argue because there’s a curtain in front of your window blocking it from your sight. But what’s behind that curtain is well documented regardless of your experience.
I mean is that so different from what we do? My boss says “tools are in the bed”, he could mean an actual bed where people sleep, maybe we’re demoing a house and he placed the tools on a bed. But probably he means the bed of his pickup truck. I assign a probability to each and take the meaning that is most probable.
Yes it is different, because you can reason that out using the context of the situation. An LLM only has the words sent to it, and no ability to analyze whether what it is saying makes sense.
It’s just: you said bed and told, here’s some other words that commonly show up near the word bed, if there’s enough smut in it’s training, it might go a very different direction than your expecting.
Thinking/reasoning tokens kind of approximate that actually, which is what most flagships and even my own local LLM use.
Thinking tokens are quite like normal generative tokens, except that the LLM is ‘talking’ to itself. You can see its thoughts (depending on what settings you’ve put/IDE you use), but they aren’t meant to be the actual response to your prompt. They are what the AI is designed to draft their answer before committing, to explore different options and to ‘reason’ itself into a more refined response.
Reasoning tokens is how AI can actually do math now, rather than just guess a number and pray, by the way.