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Fi 🏳️‍⚧️ @munin@infosec.exchange
5mo
So, something that's been bugging the shit out of me?

These fucking assholes who let LLMs run rampant and delete prod?

They query the LLM for "why" it did that.

This is delusional behavior.

LLMs do not have a concept of 'why': they assemble a response based on a statistical sampling of likely continuations of the original prompt in their database.

LLMs do not have the ability to have motivation. It is a machine.

LLMs, further, function by instantiating a new runtime -for each query- that reads the prompt and any cache, if they exist, from prior sessions:

which means, fundamentally, "asking" the LLM to explain "why" "it" did a thing is thrice-divorced from reality:

It cannot have a why;
It cannot have a self to have motivations;
And the LLM you ask is not the one that did it, but is a new instance reading from its predecessors notes.

Treating it as tho it is an entity with continuity of existence is fucking delusional and I am fucking sick of pandering to this horseshit.

Touch some grass and get a fucking therapist.
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🪨 @Varpie@peculiar.florist
5mo
@munin Well... As you mentioned, each generation "reads the prompt and any cache, if they exist, from prior session", and since they were trained on "explaining" their previous outputs to sound like a relevant discussion, asking why a model gave a specific output isn't as stupid as you make it out to be, as it can give some input on the "thinking" part of the previous output that is usually not directly visible. That can then be used to tweak prompts and add some guardrails (even though there is a fairly long list of examples of guardrails not being fully effective). Of course, the first problem is giving access to prod to an unreliable system...
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Pete Alex Harris🦡🕸️🌲/∞🪐∫ @petealexharris@mastodon.scot
4mo
@Varpie @munin
No it's still stupid, because the LLM has no semantic model of reality, only a surface statistical model of language present in the training data.

The answer to "why did you do that?" will not be about the likely state of the machine as described by a summary of the previous session, it'll be a probable continuation of text beginning with that summary and the question. That's not the same.

Words about words aren't words about things.
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🪨 @Varpie@peculiar.florist
4mo
@petealexharris @munin Clearly you've never tried it yourself...
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Pete Alex Harris🦡🕸️🌲/∞🪐∫ @petealexharris@mastodon.scot
4mo
@Varpie @munin

I think there must be some confusion here between having the anecdotal experience of being satisfied with an LLM explanation because YOU have a semantic model you can fit the words into, and the source of the explanation having a semantic model it came from.

Those are inherently not the same.

I have had to content myself with the output of AI tools at work, and when they give a useless answer, at least I know why. I don't kid myself they'd "understand" a better prompt.
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🪨 @Varpie@peculiar.florist
4mo
@petealexharris @munin Where did I say they'd understand a better prompt?
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Pete Alex Harris🦡🕸️🌲/∞🪐∫ @petealexharris@mastodon.scot
4mo
@Varpie @munin

Where did I say you'd said that? No, let's not.

To clarify: LLMs do not understand things. They don't contain an inner model of real-world entities that they construct from the prompt, manipulate to determine the answer and then generate a natural language explanation of as output. They are language all the way through, and that is inherently surface-level.

If you're asking an LLM anything all you're asking is "what words would come next" There IS no "why?" in them.
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🪨 @Varpie@peculiar.florist
4mo
@petealexharris @munin When you ask an LLM "why is the sky blue?", it is statistically likely to give a correct answer. It still works the same way, computing probabilities of what the next token is, but the "why" has a semantically significant weight that influences the output, so it is an important keyword. It doesn't have to "understand" it, it just has to be trained in a way that makes it significant. You don't have to believe that it understand things to know that it is trained on human language and will behave correctly when fed human language.
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Pete Alex Harris🦡🕸️🌲/∞🪐∫ @petealexharris@mastodon.scot
4mo
@Varpie @munin

This seems like the wrong lesson to take from Searle's Chinese Room, but whatever.

Yes, the most statistically likely answer to a common question with lots of instances of its answer in the training data will be the words of the right answer.

But move away from generalities to specific situations where what matters is details of what happened in reality, not what words were in training data? Not so good, and it can't be. It's not safe to be unaware of the difference.
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🪨 @Varpie@peculiar.florist
4mo
@petealexharris @munin "Why" is definitely a word from the training data, and "why did you do that?" is definitely also part of things asked a lot, that OpenAI and others have trained on, so my point still stands that it is a valid question to ask. Whether the model "understands" the question is just a philosophical question that is irrelevant for the fact that it is a useful question. Of course if you're using it in Prod and it deletes your DB and you think it understands and can improve itself, there are plenty of things you'd need to be corrected on, but saying that everyone asking that question is delusional is just wrong.
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Pete Alex Harris🦡🕸️🌲/∞🪐∫ @petealexharris@mastodon.scot
4mo
@Varpie @munin

Whether a language model without a real world model can provide useful answers to questions about the real world only by reasoning about the words, even if to a human those words carry meaning about the real world, is not an empty philosophical question. It's a fairly practical one.

It may be statistically likely for certain kinds of answers to follow a "why" question but that's NOT the same as answering it on the specifics of what happened.
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🪨 @Varpie@peculiar.florist
4mo
@petealexharris @munin You misread me. Whether the model "understands" the question is a philosophical question. The non-philosophical question of whether it can give a useful answer is the relevant part, and my whole point is that pointing at the philosophical aspect to belittle people that look at the practical part, assuming that they don't understand it, is dumb.
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Fi 🏳️‍⚧️ @munin@infosec.exchange
4mo
@Varpie @petealexharris

can you two take your semantics argument elsewhere; I am not interested in philosophical horseshit when there are specific, practical considerations that are causing specific, enumerable harms.
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🪨 @Varpie@peculiar.florist
4mo
@munin @petealexharris Sure, I'll go touch some grass and talk to my therapist about this philosophical horseshit ​:meow_ok_fine:​
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Pete Alex Harris🦡🕸️🌲/∞🪐∫ @petealexharris@mastodon.scot
4mo
@Varpie

Yeah that'd be rude and unhelpful.

But there isn't a philosophical question as to whether an LLM understands anything; it just doesn't. Nobody I'd take seriously wants to waste time on that.

Its limitations aren't abstract and philosophical, they're structural. When I say there's no semantic model of the real world in there, that's a structural criticism.

It lacks a construct mirroring the objects being asked about, that it can manipulate and query. It only has the words about it.
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🪨 @Varpie@peculiar.florist
4mo
@petealexharris I totally agree with you. And that is also a very different take from the beginning of the discussion, where Fi said that querying LLMs for "why" it does something is "thrice-divorced from reality" and "fucking delusional" and that people doing that should "touch some grass and get a fucking therapist"...
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Pete Alex Harris🦡🕸️🌲/∞🪐∫ @petealexharris@mastodon.scot
4mo
@Varpie
I read that as the same take expressed more colourfully.
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