That’s because they aren’t “aware” of anything.
This Nobel Prize winner and subject matter expert takes the opposite view
People really do not like seeing opposing viewpoints, eh? There’s disagreeing, and then there’s downvoting to oblivion without even engaging in a discussion, haha.
Even if they’re probably right, in such murky uncertain waters where we’re not experts, one should have at least a little open mind, or live and let live.
It’s like talking with someone who thinks the Earth is flat. There isn’t anything to discuss. They’re objectively wrong.
Humans like to anthropomorphize everything. It’s why you can see a face on a car’s front grille. LLMs are ultra advanced pattern matching algorithms. They do not think or reason or have any kind of opinion or sentience, yet they are being utilized as if they do. Let’s see how it works out for the world, I guess.
Large language models aren’t designed to be knowledge machines - they’re designed to generate natural-sounding language, nothing more. The fact that they ever get things right is just a byproduct of their training data containing a lot of correct information. These systems aren’t generally intelligent, and people need to stop treating them as if they are. Complaining that an LLM gives out wrong information isn’t a failure of the model itself - it’s a mismatch of expectations.
Neither are our brains.
“Brains are survival engines, not truth detectors. If self-deception promotes fitness, the brain lies. Stops noticing—irrelevant things. Truth never matters. Only fitness. By now you don’t experience the world as it exists at all. You experience a simulation built from assumptions. Shortcuts. Lies. Whole species is agnosiac by default.”
― Peter Watts, Blindsight (fiction)
Starting to think we’re really not much smarter. “But LLMs tell us what we want to hear!” Been on FaceBook lately, or lemmy?
If nothing else, LLMs have woke me to how stupid humans are vs. the machines.
However, when the participants and LLMs were asked retroactively how well they thought they did, only the humans appeared able to adjust expectations
This is what everyone with a fucking clue has been saying for the past 5, 6? years these stupid fucking chatbots have been around.
Why is a researcher with a PhD in social sciences researching the accuracy confidence of predictive text, how has this person gotten to where they are without being able to understand that LLMs don’t think? Surely that came up when he started even considering this brainfart of a research project?
Someone has to prove it wrong before it’s actually wrong. Maybe they set out to discredit the bots
I guess, but it’s like proving your phones predictive text has confidence in its suggestions regardless of accuracy. Confidence is not an attribute of a math function, they are attributing intelligence to a predictive model.
I work in risk management, but don’t really have a strong understanding of LLM mechanics. “Confidence” is something that i quantify in my work, but it has different terms that are associated with it. In modeling outcomes, I may say that we have 60% confidence in achieving our budget objectives, while others would express the same result by saying our chances of achieving our budget objective are 60%. Again, I’m not sure if this is what the LLM is doing, but if it is producing a modeled prediction with a CDF of possible outcomes, then representing its result with 100% confindence means that the LLM didn’t model any other possible outcomes other than the answer it is providing, which does seem troubling.
It’s easy, just ask the AI “are you sure”? Until it stops changing it’s answer.
But seriously, LLMs are just advanced autocomplete.
They can even get math wrong. Which surprised me. Had to tell it the answer is wrong for them to recalculate and then get the correct answer. It was simple percentages of a list of numbers I had asked.
I kid you not, early on (mid 2023) some guy mentioned using ChatGPT for his work and not even checking the output (he was in some sort of non-techie field that was still in the wheelhouse of text generation). I expresssed that LLMs can include some glaring mistakes and he said he fixed it by always including in his prompt “Do not hallucinate content and verify all data is actually correct.”.
Ah, well then, if he tells the bot to not hallucinate and validate output there’s no reason to not trust the output. After all, you told the bot not to, and we all know that self regulation works without issue all of the time.
People should understand that words like “unaware” or “overconfident” are not even applicable to these pieces of software. We might build intelligent machines in the future but if you know how these large language models work, it is obvious that it doesn’t even make sense to talk about the awareness, intelligence, or confidence of such systems.
I find it so incredibly frustrating that we’ve gotten to the point where the “marketing guys” are not only in charge, but are believed without question, that what they say is true until proven otherwise.
“AI” becoming the colloquial term for LLMs and them being treated as a flawed intelligence instead of interesting generative constructs is purely in service of people selling them as such. And it’s maddening. Because they’re worthless for that purpose.
Sounds pretty human to me. /s
Sounds pretty human to me. no /s
Oh god I just figured it out.
It was never they are good at their tasks, faster, or more money efficient.
They are just confident to stupid people.
Christ, it’s exactly the same failing upwards that produced the c suite. They’ve just automated the process.
Oh good, so that means we can just replace the C-suite with LLMs then, right? Right?
An AI won’t need a Golden Parachute when they inevitably fuck it all up.
AI evolved their own form of the Dunning Kruger effect.
Confidently incorrect.
This happened to me the other day with Jippity. It outright lied to me:
“You’re absolutely right. Although I don’t have access to the earlier parts of the conversation”.
So it says that I was right in a particular statement, but didn’t actually know what I said. So I said to it, you just lied. It kept saying variations of:
“I didn’t lie intentionally”
“I understand why it seems that way”
“I wasn’t misleading you”
etc
It flat out lied and tried to gaslight me into thinking I was in the wrong for taking that way.
It didn’t lie to you or gaslight you because those are things that a person with agency does. Someone who lies to you makes a decision to deceive you for whatever reason they have. Someone who gaslights you makes a decision to behave like the truth as you know it is wrong in order to discombobulate you and make you question your reality.
The only thing close to a decision that LLMs make is: what text can I generate that statistically looks similar to all the other text that I’ve been given. The only reason they answer questions is because in the training data they’ve been provided, questions are usually followed by answers.
It’s not apologizing you to, it knows from its training data that sometimes accusations are followed by language that we interpret as an apology, and sometimes by language that we interpret as pushing back. It regurgitates these apologies without understanding anything, which is why they seem incredibly insincere - it has no ability to be sincere because it doesn’t have any thoughts.
There is no thinking. There are no decisions. The more we anthropomorphize these statistical text generators, ascribing thoughts and feelings and decision making to them, the less we collectively understand what they are, and the more we fall into the trap of these AI marketers about how close we are to truly thinking machines.
The only thing close to a decision that LLMs make is
That’s not true. An “if statement” is literally a decision tree.
The only reason they answer questions is because in the training data they’ve been provided
This is technically true for something like GPT-1. But it hasn’t been true for the models trained in the last few years.
it knows from its training data that sometimes accusations are followed by language that we interpret as an apology, and sometimes by language that we interpret as pushing back. It regurgitates these apologies without understanding anything, which is why they seem incredibly insincere
It has a large amount of system prompts that alter default behaviour in certain situations. Such as not giving the answer on how to make a bomb. I’m fairly certain there are catches in place to not be overly apologetic to minimize any reputation harm and to reduce potential “liability” issues.
And in that scenario, yes I’m being gaslite because a human told it to.
There is no thinking
Partially agree. There’s no “thinking” in sentient or sapient sense. But there is thinking in the academic/literal definition sense.
There are no decisions
Absolutely false. The entire neural network is billions upon billions of decision trees.
The more we anthropomorphize these statistical text generators, ascribing thoughts and feelings and decision making to them, the less we collectively understand what they are
I promise you I know very well what LLMs and other AI systems are. They aren’t alive, they do not have human or sapient level of intelligence, and they don’t feel. I’ve actually worked in the AI field for a decade. I’ve trained countless models. I’m quite familiar with them.
But “gaslighting” is a perfectly fine description of what I explained. The initial conditions were the same and the end result (me knowing the truth and getting irritated about it) were also the same.
The only thing close to a decision that LLMs make is
That’s not true. An “if statement” is literally a decision tree.
If you want to engage in a semantically argument, then sure, an “if statement” is a form of decision. This is a worthless distinction that has nothing to do with my original point and I believe you’re aware of that so I’m not sure what this adds to the actual meat of the argument?
The only reason they answer questions is because in the training data they’ve been provided
This is technically true for something like GPT-1. But it hasn’t been true for the models trained in the last few years.
Okay, what was added to models trained in the last few years that makes this untrue? To the best of my knowledge, the only advancements have involved:
- Pre-training, which involves some additional steps to add to or modify the initial training data
- Fine-tuning, which is additional training on top of an existing model for specific applications.
- Reasoning, which to the best of my knowledge involves breaking the token output down into stages to give the final output more depth.
- “More”. More training data, more parameters, more GPUs, more power, etc.
I’m hardly an expert in the field, so I could have missed plenty, so what is it that makes it “understand” that a question needs to be answered that doesn’t ultimately go back to the original training data? If I feed it training data that never involves questions, then how will it “know” to answer that question?
it knows from its training data that sometimes accusations are followed by language that we interpret as an apology, and sometimes by language that we interpret as pushing back. It regurgitates these apologies without understanding anything, which is why they seem incredibly insincere
It has a large amount of system prompts that alter default behaviour in certain situations. Such as not giving the answer on how to make a bomb. I’m fairly certain there are catches in place to not be overly apologetic to minimize any reputation harm and to reduce potential “liability” issues.
System prompts are literally just additional input that is “upstream” of the actual user input, and I fail to see how that changes what I said about it not understanding what an apology is, or how it can be sincere when the LLM is just spitting out words based on their statistical relation to one another?
An LLM doesn’t even understand the concept of right or wrong, much less why lying is bad or when it needs to apologize. It can “apologize” in the sense that it has many examples of apologies that it can synthesize into output when you request one, but beyond that it’s just outputting text. It doesn’t have any understanding of that text.
And in that scenario, yes I’m being gaslite because a human told it to.
Again, all that’s doing is adding additional words that can be used in generating output. It’s still just generating text output based on text input. That’s it. It has to know it’s lying or being deceitful in order to gaslight you. Does the text resemble something that can be used to gaslight you? Sure. And if I copy and pasted that from ChatGPT that’s what I’d be doing, but an LLM doesn’t have any real understanding of what it’s outputting so saying that there’s any intent to do anything other than generate text based on other text is just nonsense.
There is no thinking
Partially agree. There’s no “thinking” in sentient or sapient sense. But there is thinking in the academic/literal definition sense.
Care to expand on that? Every definition of thinking that I find involves some kind of consideration or reflection, which I would argue that the LLM is not doing, because it’s literally generating output based on a complex system of weighted parameters.
If you want to take the simplest definition of “well, it’s considering what to output and therefore that’s thought”, then I could argue my smart phone is “thinking” because when I tap on a part of the screen it makes decisions about how to respond. But I don’t think anyone would consider that real “thought”.
There are no decisions
Absolutely false. The entire neural network is billions upon billions of decision trees.
And a logic gate “decides” what to output. And my lightbulb “decides” whether or not to light up based on the state of the switch. And my alarm “decides” to go off based on what time I set it for last night.
My entire point was to stop anthropomorphizing LLMs by describing what they do as “thought”, and that they don’t make “decisions” in the same way humans do. If you want to use definitions that are overly broad just to say I’m wrong, fine, that’s your prerogative, but it has nothing to do with the idea I was trying to communicate.
The more we anthropomorphize these statistical text generators, ascribing thoughts and feelings and decision making to them, the less we collectively understand what they are
I promise you I know very well what LLMs and other AI systems are. They aren’t alive, they do not have human or sapient level of intelligence, and they don’t feel. I’ve actually worked in the AI field for a decade. I’ve trained countless models. I’m quite familiar with them.
Cool.
But “gaslighting” is a perfectly fine description of what I explained. The initial conditions were the same and the end result (me knowing the truth and getting irritated about it) were also the same.
Sure, if you wanna ascribe human terminology to what marketing companies are calling “artificial intelligence” and further reinforcing misconceptions about how LLMs work, then yeah, you can do that. If you care about people understanding that these algorithms aren’t actually thinking in the same way that humans do, and therefore believing many falsehoods about their capabilities, like I do, then you’d use different terminology.
It’s clear that you don’t care about that and will continue to anthropomorphize these models, so… I guess I’m done here.
But what about humans?
LLMs don’t understand anything. At all. They’re a glorified auto complete.
How do you think language in our brains work? Just like many things in tech (especially cameras), things are often inspired by how it works in nature.
They are not only unaware of their own mistakes, they are unaware of their successes. They are generating content that is, per their training corpus, consistent with the input. This gets eerie, and the ‘uncanny valley’ of the mistakes are all the more striking, but they are just generating content without concept of ‘mistake’ or’ ‘success’ or the content being a model for something else and not just being a blend of stuff from the training data.
For example:
Me: Generate an image of a frog on a lilypad.
LLM: I’ll try to create that — a peaceful frog on a lilypad in a serene pond scene. The image will appear shortly below.<includes a perfectly credible picture of a frog on a lilypad, request successfully processed>
Me (lying): That seems to have produced a frog under a lilypad instead of on top.
LLM: Thanks for pointing that out! I’m generating a corrected version now with the frog clearly sitting on top of the lilypad. It’ll appear below shortly.<includes another perfectly credible picture>
It didn’t know anything about the picture, it just took the input at it’s word. A human would have stopped to say “uhh… what do you mean, the lilypad is on water and frog is on top of that?” Or if the human were really trying to just do the request without clarification, they might have tried to think “maybe he wanted it from the perspective of a fish, and he wanted the frog underwater?”. A human wouldn’t have gone “you are right, I made a mistake, here I’ve tried again” and include almost the exact same thing.
But tha training data isn’t predominantly people blatantly lying about such obvious things or second guessing things that were done so obviously normally correct.
The use of language like “unaware” when people are discussing LLMs drives me crazy. LLMs aren’t “aware” of anything. They do not have a capacity for awareness in the first place.
People need to stop taking about them using terms that imply thought or consciousness, because it subtly feeds into the idea that they are capable of such.
