Knowledge cutoff refers to the "guaranteed knowledge". Scrapes may have information beyond this point since internet scraping is presumably done over many days.
For instance, this particular version of GPT4 is aware of the date Russia invaded Ukraine (Feb 24, 2022).
I suspect you need to ask questions that resolved >3 months past the knowledge cutoff date for GPT-4 to truly not know.
This is an interesting premise that should pursuit further testing on base models rather than instruction-enhanced ones. The assumption that LLMs with all its knowledge is able to ballpark a potentially quantifiable guess is not illogical.
There's not really any reason to think this other than shibboleths, it's become a thought-deadening cliche.
I've played with true base models at big tech over a period of 6+ months, it's a much much more frustrating experience, not an "unlocked" or "honest" version. Setting temperature to 1.2 is a reasonable miniature taste of the vibe of it.
It would be interesting to see it done using logit probabilities, I.e. instead of asking for p 0 to 1, instead, ask it true or false, set logit bias such that those are the only two tokens that could be output, and use the logit probability emitted by the API.
> There's not really any reason to think this other than shibboleths, it's become a thought-deadening cliche.
The official OpenAI GPT-4 paper specifically reporting that the RLHF model is badly uncalibrated, while the GPT-4 base mode is pretty well calibrated, is a good reason to think that, and is not a 'thought-deadening cliche shibboleth': https://arxiv.org/pdf/2303.08774.pdf#page=12 "The post-training hurts calibration significantly."
I would ask it to justify its decisions in detail and ask an expert to judge the reasoning. I'm still routinely disappointed by LLMs failing basic reasoning tasks.
Most LLMs fail easy tests like "A is faster than B, B is faster than C, is C faster than A?" and questions about a ball in an upside-down cup. Better models get those but fail at other “common sense” tasks. One example from the very end of the OpenAI keynote, the demo when they granted credits first to five people and then to everyone: did the AI know not to credit the five lucky recipients twice?
Just like how bacteria has no independent "awareness" (as far as we know). And yet, they are partaking in darwinian selection.
It's just in the world of LLM, it's not nature but artificial, and that "reproduction" is not simple and random like it would be in nature, but via careful selection of humans.
Because while the model at baseline is a regression towards the mean, it is easily biased based on additional context - one of the big eye openers in research over the past 18 months has been the impact and success of in context learning alone.
So with an LLM you have a very broad basis that can be biased into a niche application in a single unit whereas with humans you have a fairly static set of capabilities that are widely and normally distributed across individual units.
You can be lucky in finding the right unit for the task with humans that are extremely good at extending a given context. But you need to have been lucky with matching the unit's performance with the right context. A different unit will suck.
But LLMs given enough coverage in the training set and enough additional context to bias towards that subset of the training set can be consistently good (and will likely continue to get better).
'Wisdom of the crowds'. Any individual human is uncalibrated (although this is fairly easy to train away) and makes errors, but a LLM is predicting the distribution of all human responses as reflected in the corpus. So it reports a reasonable distribution.
Absolutely, unless you want to make any life-changing decisions based on them.
This is also part of the modern culture - producing useless verbiage is ok on social media, and even is scientific communities as "research papers".
In reality though no accurate predictions can be made from merely information which is what an indexed text is.
There are a few fundamental principles at various levels behind this statement.
One is that the actual causes of events are at a different level from the texts. Another is that mere observations and counting (weighting) will always miss a change, and so on.
There is also the notion of "fully observable" environment, which is related to why validity of the "prediction" about the Sun to rise tomorrow is based not on mere "statistics" but on knowing the dynamics within the Sun (the actual process).
But, yes, everyone is just riding the hype though.
It should be able to predict the outcomes of human behaviours in specific cases, such as elections.
There is enough knowledge out there on how humans behave, both from examples of past events and scientific knowledge from studies.
Then for elections there is enough human content about how humans are behaving leading up to the event. Elections specifically are long events that people are very vocal about. If you try to use it for something like 'will the demand for toilet paper go up or down this week?' you aren't going to see the same results as there is not enough data in social media.
From that an LLM should be able to predict - well extrapolate - the outcome, basically a glorified opinion poll. This however would need a near real-time knowledge cutoff, so it's not something that current LLMs could do.
This wrong idea, for me, encapsulates everything wrong about AI generally as well.
Human beings are not predictable; we're not automatons that can produce reliable outcomes. We're simply too random.
This is why a "sentient AI" or whatever silliness won't take over the world; not that it isn't smart, but at some point it would have to give orders to humans, who can't be relied on to predictably execute them correctly.
If I were to do this, I would ask the LLM to come up with a model that would predict the output and then to estimate the parameters and calculate the prediction (and show the work).
For all we know, this is what's happening inside the black box (if it's really simulating a super forecaster), but from experience, it's easier to get what I want from LLMs if I have it talk a little about what it's doing.
A better task would be to ask the LLM what information they need to check to have a better guess and offer constraints like publicly available information or information that doesn’t cost more than a certain amount to get.
A real expert isn’t someone who knows the calorific coefficient of water off the top of their head but knows it’s relevant information to look up when asked if a cargo ship full of feedstock could burn and vaporize the water in the Suez Canal.
They are excellent predictors of what they have been taught to predict: composing text in different languages. They are so proficient at these predictions that it is challenging to distinguish LLMs text from human-written text. As a byproduct of learning from Common Crawl, they are adept at predicting average "internet-human" behavior, leading to phenomena such as "Sydney". Their predictive capabilities are so impressive that you can play chess with them and find a formidable opponent. Despite never having seen anything beyond the text in their training data, they demonstrate spatial comprehension. They are universal approximators...
The chess stuff has been pretty wild, particularly in how users claim it plays more "human-like" than chess programs.
I still remember the eerie feeling when Copilot suggested something in a personal codebase that reflected something fairly unique to my own thinking elsewhere in the code but did so about a minute before I was going to think of it.
It's one thing when GPT is approximating humanity at large.
I think "main street" is going to have yet another WTF moment when integration into email clients, chat, document suites, etc suddenly start approximating their own unique thinking, phrasing, inside jokes, etc.
It's pretty amazing tech and only getting better, even if it still has a long way to go before I'd ever feel comfortable in handing off the reigns to compose things for me without additional review.
There’s a difference between predictions which can be reasonably ascertained from the inputs and those which cannot. No language model can tell you the outcome of a fair coin flip.
If you want a reasonable test, you can test autoregressive models on out of domain tasks like MNIST classification after pasting raw pixel values into the chat. This is a meaningful test because there’s enough information in the input to determine the outputs (any CS undergrad can train a model to do this).
For the cases tested in the article, we can’t tell if there’s just not enough information to make a decision, or if the task is too far out of domain. Given GPT’s performance in other out of domain tasks, it’s likely the issue is both in this case.
There must be oodles of good "chess via text" in the training data. So they're pretty good at chess.
But, without even checking, I know that all the real-world predictions in the training data (written by humans) is utter garbage. And that's without even assuming that 4chan (or 4chan-quality) text slipped into it. Humans are bad at predicting the real world, and convinced thoroughly that they're good and it's just other people who are bad.
If one could prepare a training dataset large enough, with good predictions (which aren't necessarily correct predictions), would an LLM trained on that become good at it?
[edit] This is a (bad?) assumption that humans could be good predictors, and that there's not something that just rules it out entirely. If they could, and if you could prepare the dataset of that. If they can't because physics disallows it or something, then LLMs which crudely mimic how humans predict these things will also find it impossible.
I don't think it's impossible to be good at prediction (should be possible, at least if we narrow the categories a bit), I just think we all suck at it.
There's a community project in the Eleuther ai discord that has trained several pythia models on chess games and come out playing it just fine. and they play better with increased scale too.
Then recently Open ai released GPT-3.5 turbo instruct and that plays chess reliably at around 1800 Elo.
So i don't know about "notoriously bad". People probably just overestimate how much chess game data most language models are actually getting. That or chat RLHF tanks chess abilities.
A lot of the issues are related to the model trying to generate bad/illegal moves.
Far better is to do RAG and give it a list of the valid moves given the current board state. Might be able to exploit current computer chess formats to do this more compactly in the prompt.
I expect above 2000 elo if you do what I am describing with GPT4.
The fact that it’s making illegal moves at all should tell you something. The model may be able to suggest reasonable seeming moves, but it can’t even understand the rules of the game or the current board position.
Maybe ChatGPT plays at an 1800 elo but how many 1800 players regularly have a hard time making legal moves? Limiting ChatGPT to legal moves only might help, but it’s ultimately a band aid.
Grandmasters do not make an illegal move every 300 moves. That is noticeably higher than what we would expect, and it should give us pause when evaluating how 3.5-turbo works and how it's evaluating chess moves.
If an 1800 elo human did that, we would find the behavior to be odd and we would question how they learned to play and what their decision-making process was when making moves.
"Thing happening commonly in scenario X can also rarely happen in scenario Y, therefore both scenarios are exactly the same" is one of the more annoyingly reductive arguments to have come out of recent AI discussions. You have to actually take frequency into account when you're talking about this stuff, and events occurring at abnormal or atypical frequencies can communicate information about the world.
>Grandmasters do not make an illegal move every 300 moves.
Sure. Point is that even the best players will make an illegal move (even if rarely) so a computer that makes an illegal move is not grounds for "does not understand chess" just because it doesn't always make a legal move.
He says, "The fact that it’s making illegal moves at all should tell you something." He is literally making the assertion that a single instance of an illegal move means you don't understanding how to play the game. This is trivially proven false.
>If an 1800 elo human did that, we would find the behavior to be odd and we would question how they learned to play and what their decision-making process was when making moves.
Yes because humans usually learn the rules first then play. If a human learnt chess from watching games, i would not find this behavior odd at all. In fact, i would expect it. Just because you've never seen a move played before does not mean the move is illegal.
Moreover, odd as it may be, I still wouldn't call it "struggling to make a legal move".
>"Thing happening commonly in scenario X can also rarely happen in scenario Y, therefore both scenarios are exactly the same" is one of the more annoyingly reductive arguments to have come out of recent AI discussions.
> Sure. Point is that even the best players will make an illegal move (even if rarely) so a computer that makes an illegal move is not grounds for "does not understand chess" just because it doesn't always make a legal move.
You're just making the exact same mistake a second time in a row. It is evidence; maybe not full on undeniable 100% proof, but it should shift your priors about how you think about AI, and if it doesn't I would say you're ignoring obvious statistical evidence.
An analogy: sometimes cell phone batteries explode. If 1/300 cellphone batteries for a specific brand start exploding, you would not say, "that's not proof of anything, all cell phone batteries have a risk of exploding." You would say, "huh, something is probably going very wrong at the factory."
Observing an abnormally high rate of something that should be much more uncommon is actually evidence that something could be going wrong with the underlying process. Observing that a computer has an abnormally high rates of illegal moves is evidence that it struggles to actually understand the rules of chess and that it might be imitating play instead of understanding it.
> Yes because humans usually learn the rules first then play.
That feels like you're agreeing with the original poster? The computer is imitating play that it's seen before, it's not starting from a baseline understanding of how chess works or why those moves are being made.
I would say a human grandmaster who regularly makes illegal moves at a rate of 1/300 does not have as deep or complete of an understanding of the game as other grandmasters who do not make those errors at nearly the same level or frequency. I think that's what most people would say about that person.
>You're just making the exact same mistake a second time in a row. It is evidence; maybe not full on undeniable 100% proof, but it should shift your priors about how you think about AI, and if it doesn't I would say you're ignoring obvious statistical evidence.
Anything can be called a mistake if you ignore crucial context. I replied to someone else addressing very specific points he/she brought up.
>would say a human grandmaster who regularly makes illegal moves at a rate of 1/300 does not have as deep or complete of an understanding of the game as other grandmasters who do not make those errors at nearly the same level or frequency. I think that's what most people would say about that person.
A GPT grandmaster would make even less mistakes. That aside because I understand it's not your main point, again this is more nuance than who I was replying to. You have said "does not have as complete an understanding" where the person I replied to said "does not understand the game at all". I feel like you want to argue against a point that was not present in the original discussion I was having.
>That feels like you're agreeing with the original poster?
The original poster is lacking the nuance to have me agree with him.
We're kind of splitting straws here. Whether GPT understands some of the rules of chess or whether it's completely relying on modulation between states, or whether it's using some other strategy -- it seems to be pretty obviously approaching chess using a different strategy than a human being would.
And I think that's the main point that janalsncm was getting at -- GPT thinks about the world and thinks about predictions and thinks about subjects like chess differently than a human would. It does not appear to "understand" chess in the same way that a human does when approaching the game.
Of course, you can have different definitions of understanding, GPT might be looking at chess more as a series of patterns or thinking about the shape of a game in a way that a human might not. But the value in understanding the distinction is that GPT is going to be good at some things that a human is bad at, and bad at some things that a human is good at, and understanding that it approaches tasks differently than humans is key to understanding why GPT struggles or succeeds at certain tasks.
I'm not going to run around defending other people's comments but for whatever it is worth, I don't think janalsncm's comment was unreasonable or lacked nuance. It didn't go out of its way to clarify that GPT isn't useless, it didn't go out of its way to say that it's still impressive that a language model can get the performance that it does get, but the basic idea of "hey, this is acting different than a human and maybe it shouldn't be directly compared to a human, and the deviations in its behavior should tell us something about the model" is sound.
It's not just sound, it's also useful advice for people to internalize. On a purely practical level, you will get much better results out of GPT if you modify your prompts and approaches to cater to its strengths rather than its weaknesses. To do that, you have to understand what the weaknesses and strengths are and you have to understand that imitation of human behavior does not necessarily imply that GPT is approaching or understanding those tasks in remotely the same way that a human being would. It is useful information for prompting GPT and you will get better results out of GPT if you understand that it's very often not approaching tasks from first principles.
And chess is frankly a really good example of that, it's a great example of a task where the end result initially suggests that humans and LLMs are approaching a task identically, and then upon further examination reveals that in all likelyhood, the model does not have the same first-principles or understanding of the rulesets that a human player does. It's striking because for a human to get to the same level of play they would need to have a fundamental understanding of the rulesets, and GPT is able to get to that level of play seemingly without building that level of understanding. That is an interesting thing to learn about GPT.
I don't know how relevant this argument even is anymore considering I was wrong about the move rate. Is 1 in ~1650 really underperforming a 1800 Elo Human ? How much so ?
>if you understand that it's very often not approaching tasks from first principles
You don't how humans play chess ie what the brain computes. You don't know what "first principles" actually are for intelligence or reasoning or whatever.
If "first principles" were what we thought they were, we wouldn't be using GPT or artificial neural networks for nearly all computer intelligence tasks. We would be using symbolic systems.
Unfortunately the "first principles" systems we labored for decades to build mostly failed and we abandoned them once NNs had the compute to work.
Either we have no clue what these "first principles" are or that intelligence cannot generally be delineated as a system of "first principles"(ie intelligence is not actually logically tractable). My bet is both. Intelligence is not logically tractable and because we have no idea how intelligence works, we foolishly assumed so
Either way, Humans or any other animals we've observed are certainly not first principling their way out of chess or really anything.
> I don't know how relevant this argument even is anymore considering I was wrong about the move rate. Is 1 in ~1650 really underperforming a 1800 Elo Human ? How much so ?
Yes, I would still be surprised if an 1800 elo human makes 1/1650 illegal moves during formal matches.
> If "first principles" were what we thought they were, we wouldn't be using GPT or artificial neural networks for nearly all computer intelligence tasks. We would be using symbolic systems.
This would only be true if you believe in a singular definition of intelligence where you believe there is only one right way to get an answer. There are lots of reasons we don't train AI from first principles, one of the biggest being that neural networks are just flat-out easier to train. Training from first principles is annoying and time consuming and requires either an understanding of first principles or some simulation or methodology to consistently demonstrate those principles to the AI. That is hard to do. But first principles and predicate logic haven't failed as a way to accomplishing tasks or as a way to reason about problems. First principles are ill-suited for an extremely specific field.
And now people are acting like however AIs work that has to necessarily match how humans learn? That's not the case, what I am getting at when I talk about diversity of intelligence is that you don't need to map to human reasoning in order to build tools that are useful and that generally solve certain categories of tasks, sometimes even better than humans do.
It's incredibly limiting to define what kinds of intelligence do and don't exist in the world based purely on what is useful for training an AI.
----
Humans observably do not learn the same way as GPT does, we do not start out by learning language and then logic, we do the opposite. Any study of any infant will show you that humans develop logical capabilities before we develop language skills. However that process is happening, it factually, observably happens in the opposite order than what we see occurring in LLMs.
We do not need to know exactly how that process works to know that it is different from how GPT learns. And we certainly don't need to look at a poorly understood process and assume that it necessarily must match whatever the current state-of-the-art methodology we have to simulate it. As you accurately say, we do not know how to define intelligence broadly -- so it's wildly reductive to say "but we do know how to build neural networks, and when we tried to replicate first principles or composite logic that didn't work well, so probably that doesn't exist" -- I just don't view that as a serious take.
I still feel like you're falling into the trap of getting really invested into showing that GPT is meeting some human standard of reasoning, and I don't understand why it matters whether or not GPT is meeting that standard. Why is it a problem to say, "we don't understand how humans work, but we're seeing surprising differences between GPT's capabilities and the capabilities of comparable humans, and that suggests there is something very different going on under the hood."?
Does that actually make GPT any less interesting or useful? I don't think so.
>This would only be true if you believe in a singular definition of intelligence where you believe there is only one right way to get an answer.
The whole idea of the first principles movement was that it was the "right way" anchored by how humans supposedly approached problems and many of these brilliant minds thought intelligence could not exist without "symbolic manipulation".
Well it wasn't "the right way" because it mostly failed and even the little niches it did work out produced systems that didn't behave anything like we did.
Anything other than clear definitions and unambiguous axioms (aka the real world) and logic systems fall apart.
I don't need to believe there is only one path to intelligence (I don't) to see the first principle way just doesn't cut it. General Intelligence will never happen this way. Narrow real world intelligence will never happen this way. Decades of the brightest minds toiling and some people still don't seem to get it. Logic scales poorly if your problem space isn't exactly defined.
>There are lots of reasons we don't train AI from first principles, one of the biggest being that neural networks are just flat-out easier to train.
The single biggest reason is that it doesn't work for most aspects of intelligence we observe in the real world.
>Training from first principles is annoying and time consuming, but it hasn't failed as a way for accomplishing tasks
Yes it has.
>it was ill-suited for an extremely specific field.
Ah yes, the very specific fields of language, vision and speech synthesis and understanding, reinforcement learning, OCR and many more.
These were all things attempted with GOFAI systems prior to NNs. Not only that, these systems had decades of a headstart. Symbolic systems were not the underdog here. They were discarded because they struggled to succeed.
Symbolic NLP had gone by the wayside long before even transformers and GPT arrived. That's how poor they were.
Like Frederick Jelineck famously said, "Every time I fire a linguist, the performance of the system goes up."
First principles are an idea that never actually left the realm of fiction.
>Humans observably do not learn the same way as GPT does, we do not start out by learning language and then logic, we do the opposite.
This doesn't actually matter. A language model is trying to predict the next token. It is trying to reverse engineer the computation that could have led to an utterance. It is trying to model the casual processes that led to the text. It's not trying to draw shadows. It's trying to build the object whose shadow is a result.
I'm not saying this means GPT is a human or is doing everything the human way(whatever that may be) but gradient descent to predict a language corpus and evolution after millions of years(which is itself just another dumb optimizer not first principling anything) can converge on the same solution.
Babies will communicate coherently much earlier in a non speaking language so vocal production is obviously a huge barrier that has nothing to do with language understanding itself.
Humans not formally trained in logic are typically very bad at it. We're obviously not logic creatures.
This is exactly my point. How humans think they work are how they actually work are very different things entirely.
> Well it wasn't "the right way" because it mostly failed
"Failed" and "we weren't able to replicate it in a simulator" are two entirely different things.
> [...] Yes it has.
Based on what? If your only criteria here is what is useful for AI, then you're taking an extremely reductionist stance on what intelligence is.
> Ah yes, the very specific fields of language, vision and speech synthesis and understanding, reinforcement learning, OCR and many more.
No, the very specific field of AI. Holy crud. Even within computing, first-principle approaches are wildly useful as soon as you step outside of the field of AI, first-principle systems produce better and more accurate simulations, are better equipped to handle emergent systems, they're better equipped for complicated tasks like physical rendering. Outside of AI, nobody doubts that efficacy of first-principle reasoning about the world.
As a way to model the world, first principle reasoning and simulations work great. And if your only definition of what does and doesn't work as a way to reason about the world is "what works with AI" then... sure, whatever. But the world is bigger than AI.
> The single biggest reason is that it doesn't work for most aspects of intelligence we observe in the real world.
You're making an argument here about whether or not symbolic representations and first-principle reasoning is useful for AI. That... isn't what I'm talking about. I can't stress enough how reductive and narrow it is to approach a conversation about methods of reasoning through the lens of "if it's not useful for AI, then it's not real."
And even if first-principle reasoning was completely useless, that has nothing to do with whether or not GPT works the same way as a human being. This misses the broader point that however you think the human brain works, it very much appears to be modeling the world and learning about the world differently than GPT.
First-principle reasoning isn't the only way that humans learn of course, but we do obviously teach people information about the world based on first principles. There is going to be a lot of that in any school setting. But whatever, let's throw that all out the window; fine. Let's say that humans don't learn anything at all through examining and building on first-principles.
However it is that humans do learn, the end result is different from GPT in observable ways. Chess is a good example of that.
----
> This doesn't actually matter.
I mean... it doesn't matter unless you want to be able to make accurate predictions about what the system will and won't excel at, or want to engage with the nuances of how the system works beyond the broad strokes. Are you seriously suggesting that it's of no importance that we figure out how large neural networks like GPT work internally? I don't think any serious AI researcher would agree with that.
> This doesn't actually matter. A language model [not] is trying to predict the next token. It is trying to reverse engineer the computation that could have led to an utterance.
I don't think OpenAI researchers would agree with you on this. Yes, GPT is building heuristics and models that allow it to better predict an utterance. It is not clear that it has any motivation to accurately model the underlying reality that led to that utterance. To be clear, we don't know how GPT models the world internally or what it models.
It is a leap of faith to say that it is optimizing its internal models for accuracy rather than usefulness or general applicability. That is expressing a lot of confidence that I have not seen shared within general AI research or writing.
> I'm not saying this means GPT is a human or is doing everything the human way (whatever that may be)
Okay, we agree! That's literally all anyone is saying here, GPT does not appear to approach chess the same way that a human does and doesn't think about the game the same way that a human does. It does not "understand" the game in an identical way to how humans understand the game. That doesn't mean it's not doing anything, it just means it's not thinking using the same systems we use.
If you view that as an insult to GPT or like it's some kind of contest, that's on you. The only thing that's actually being said is that humans and GPT think about and understand chess differently.
----
> but gradient descent to predict a language corpus and evolution after millions of years(which is itself just another dumb optimizer not first principling anything) can converge on the same solution.
Citation needed. I believe it can result in very powerful solutions, I don't see any reason to believe that pure language prediction and evolution, two algorithms that are fed different inputs and optimize in different ways would produce identical results. Evolution itself doesn't produce identical results when different inputs and environments are supplied -- even within the exact same system with the exact same training strategy, you get resulting intelligences that are wildly different.
Again, since I have to clarify this, I'm not bashing LLMs when I say that. I'm saying that evolutionary pressures based on real-world inputs are different from fitness functions. I don't really see how that would be a controversial thing to say.
> Babies will communicate coherently much earlier in a non speaking language so vocal production is obviously a huge barrier that has nothing to do with language understanding itself.
You're not going to be able to get around this. Babies exhibit logical inferences about the world before they exhibit the ability to understand language. This is true if you teach a baby sign language, it's true if you try to judge its auditory or verbal vocabulary. In addition, when a child gets to be 5 years old, they exhibit logical behavior beyond their linguistic abilities. Their logical reasoning progresses faster than their language skills.
Now, contrast that with a small LLM. A 7B LLM possesses almost perfect language skills, but very limited reasoning ability. The conclusion? LLMs learn language faster than reasoning, their reasoning capabilities are an emergent property from their language skills, not the other way around.
But humans do not learn the same way that an LLM learns. We do not primarily learn by predicting language tokens. Predicting language tokens is a strategy for a kind of learning, but it's not the one that humans use. In fact, if you try to teach a human how to read though token prediction, they become illiterate. There was just a giant scandal about this a while ago where reading scores dropped dramatically when teachers moved away from first-principles phonics-style teaching to a predictive model where they asked kids "what do you think the next word might be?"
We are doing something different. Whether AI can replicate that... who cares? It genuinely does not matter if GPT works the same way as a human being does. Lots of tools work differently from humans and are still useful. But it's different, and understanding that it's different can help us use the tool more effectively.
>No, the very specific field of AI. Holy crud. Even within computing, first-principle approaches are wildly useful as soon as you step outside of the field of AI, first-principle systems produce better and more accurate simulations, are better equipped to handle emergent systems, they're better equipped for complicated tasks like physical rendering. Outside of AI, nobody doubts that efficacy of first-principle reasoning about the world.
I never said first principle approaches were useless. I said they failed to model intelligence beyond a small niche and it wasn't for a lack of trying.
>I mean... it doesn't matter unless you want to be able to make accurate predictions about what the system will and won't excel at, or want to engage with the nuances of how the system works beyond the broad strokes. Are you seriously suggesting that it's of no importance that we figure out how large neural networks like GPT work internally? I don't think any serious AI researcher would agree with that.
You don't need to figure out how GPT works internally to make useful predictions on what it will and won't excel at. Humans haven't figured any of that out and we're not doing too bad. But that's not why i said, "It doesn't matter"
I meant that what dumb optimizer you use is not as big a deal as you think. a solution isn't some copyrighted piece. There's no solution police. 2 completely different optimizers could well stumble on the same solution.
>Citation needed. I believe it can result in very powerful solutions, I don't see any reason to believe that pure language prediction and evolution, two algorithms that are fed different inputs and optimize in different ways would produce identical results.
I didn't say anything about would. I said it could.
> This doesn't actually matter. A language model [not] is trying to predict the next token. It is trying to reverse engineer the computation that could have led to an utterance.
>I don't think OpenAI researchers would agree with you on this. Yes, GPT is building heuristics and models that allow it to better predict an utterance. It is not clear that it has any motivation to accurately model the underlying reality that led to that utterance. To be clear, we don't know how GPT models the world internally or what it models.
You added [not]. I did not intend that. A language model is trying to predict the next token. I am simply telling you what that means. It means modeling the underlying structure implicit in the creation of the data. It means that if you give a model a sequence of proteins only and nothing else, biological function and structure (secondary structure, contacts, and biological activity etc) will emerge in the inner layers. This is what it takes to predict the next token. https://www.pnas.org/doi/10.1073/pnas.2016239118
On the contrary, that is exactly what ilya sutskever (open ai's lead scientist) believes. Watch 3 minutes from here.
> I said they failed to model intelligence beyond a small niche and it wasn't for a lack of trying.
This is just rephrasing "it's not useful for AI." When you say "model intelligence", what you're referring to is AI. I think this is somewhat circular. You're evaluating what techniques are useful for AI. Then you're looking at systems outside of AI and saying "those systems aren't relevant to human learning and can't be how humans learn, because it's not how we build AI."
Do you not see the problem in that logic?
> You don't need to figure out how GPT works internally to make useful predictions on what it will and won't excel at.
The irony of you saying this immediately before you claim:
> It means modeling the underlying structure implicit in the creation of the data. [...] This is what it takes to predict the next token.
Does prediction require an accurate model of the world or not?
I don't claim that you can't work with GPT unless you understand it. My claim is that there's value in understanding it. Understanding the ways it differs from humans will help you work better with GPT. I think that's a fairly uncontroversial claim, there are not a ton of systems where understanding the underlying mechanics doesn't help with performance in some way. It's not essential, but it helps.
If your argument is that modeling the underlying structure that created the data is essential to predicting that data, then it really seems like you should be agreeing with me that understanding the underlying structure that creates GPT output (ie, GPT itself) is important. Honestly, that's a stronger claim than I'm making, I'm just saying it's helpful to know what's going on: you're saying that output prediction inherently requires a model of the systems that created the inputs.
Or are you not saying that? In which case, why are you so confident that GPT is modeling the systems that create its inputs? Is this a requirement or not?
> Humans haven't figured any of that out and we're not doing too bad.
Citation definitely needed, understanding the mechanisms of how humans learn is a huge area of research and would be profoundly useful for us to understand. I mentioned earlier that we decreased reading scores for an entire generation literally because of a bad theory about how humans learn to read. These errors can have large consequences and (in the case of modeling and predicting human behavior) regularly do. That is why there are entire scientific fields dedicated to trying to answer these questions.
----
> On the contrary, that is exactly what ilya sutskever (open ai's lead scientist) believes.
Fair, I didn't realize Ilya was making claims like this. I don't think that's a particularly defensible claim, but fair.
It does bring us back to the original question: if GPT is modeling the world of chess in its answers, why does it make illegal chess moves at a significantly higher rate than humans? Why does it play chess differently from humans?
Is it bad at chess, or... perhaps the way it models chess is fundamentally different from how humans model chess? The simple take is that if you have something hitting 1800 elo but it plays in a way that looks alien to how humans play, it's probably doing something different under the hood.
----
There's some general inconsistency popping up here. Just to emphasize this again: you're simultaneously saying that multiple approaches to task-solving are possible and that we don't need to look at the internals of LLMs or think about how they learn because dumb optimization converges on the same "solution" anyway (the fact that "intelligence" is not a singular solution in the first place is a separate conversation to have). Then you're citing research within LLM communities arguing that human thought patterns mirror LLMs (which I will note, is not the predominant view of most AI researchers) and are arguing that accurate predictive models necessarily must end up accurately modeling and understanding the world in order to make predictions.
Those two claims don't jive with each other. If you're arguing that multiple approaches to prediction are possible and that they don't require knowledge or understanding of the underlying systems, then why do you think it's impossible for an AI to make predictions without accurately modeling the world? If you're arguing that making more and more accurate predictions about the world fundamentally requires an AI to understand the underlying mechanisms of the world, then why are you so laissez-faire about efforts to understand how GPT works when making predictions about its capabilities?
----
My personal take is that we have a system that produces results but produces them in a manner that looks pretty different from what we would expect from humans in the same situation. That probably means that it is using a different method to achieve results. That differing method might affect its performance, and understanding that can be useful for working with the system. For example, you might place a greater emphasis on confirming move legality when using GPT for chess. Or, to be more blunt, you might hook it up to REPL to make sure the code it produces actually compiles because it hallucinates APIs more often than humans do. Or, because you realize that it can't go back and revise previous answers and is creating a predictive output that currently only flows in one direction, you might give it prompts that specifically ask it to break its codegen down into multiple steps instead of fleshing out the entire module. Ie, all of the strategies that are commonly agreed on by pretty much everyone to get GPT to produce better code today; strategies that do not universally map to optimal coding strategies for humans.
I think it's pretty clear that GPT works differently than humans do, and I'm not even certain you disagree with that? You don't seem to be saying that you believe GPT is identically structured to a human brain. One of the differences between GPT and humans appears to be that GPT doesn't always model an underlying reality and think through its answers from first principles. Which again, you seem to agree with! You don't believe that GPT uses first principles when thinking through its answers, right?
Well, apparently controversially, if you give a 7th grader something like an algebra problem, they do use first principles; first principles is how we teach math to kids. It's not the only way that human brains work and it's probably not the core way we understand the world -- we have a lot of stuff happening in our heads; they're complicated and no one really understands human intelligence any more than OpenAI understands what specific algorithms GPT is using for predictions. But (as you yourself have pointed out) we don't teach GPT how to do math the same way we teach a 7th grader, we don't use first principles when teaching GPT. The learning mechanisms and the final strategies are different.
>This is just rephrasing "it's not useful for AI." When you say "model intelligence", what you're referring to is AI. I think this is somewhat circular. You're evaluating what techniques are useful for AI. Then you're looking at systems outside of AI and saying "those systems aren't relevant to human learning and can't be how humans learn, because it's not how we build AI."
I'm not talking about AI. I'm taking about intelligence in general. Most Humans would be trashed on a formal logic test. Yes we can be logical no doubt but it's pretty clear we're not running off logic.
If you have an idea of how something works and set off building one from that idea then failing to succeed calls into question the validity of that idea. This is science at its core.
We couldn't really build artificial intelligence the way we thought intelligence worked. So does intelligence really work this way ? That is the crux of the issue.
Intelligent creatures that run off logic basically only exist in fiction. Humans don't do it. Animals don't it. The machines we tried to build to do it this way mostly didn't work. The ones that did work are perfect and never err, more hints humans don't work this way. So we basically have no real indication that logic is at the heart of intelligence.
>Does prediction require an accurate model of the world or not?
Performant Prediction just requires a model, completely accurate or not. Newton's model of gravity is wrong but it's useful fiction so it even still gets taught in schools. Einstein's model is better than Newton's but is still wrong (does not reconcile with quantum mechanics). Doesn't stop it from making useful predictions.
What I'm saying is:
1. Prediction requires a model. The model doesn't have to be "true" to be useful or performant.
2. Perfect Prediction requires a perfect model.
What is a Language Model's goal ? What is the loss trending down to ?
It's not to make useful predictions and stop. It is to make perfect predictions of the data they are presented (so of course perfect here doesn't mean a perfect view of the world but rather a perfect view of the world as humans see it)
Everytime the machine uses its existing model to make an erring prediction, it's model is quite literally adjusted and changed to accommodate this error. But by bit this happens.
As a result, what GPT-2 computes is wildly different from what GPT-3 computes and that is different from what GPT-4 computes.
The goal is not to play chess. GPT is not going to stop on some arbitrary line of competency or error rate someone may draw up.
The goal is to model the chess dataset it's been given.
In Evolution, the pressures of two creatures can be just to fly. Plenty of wiggle room.
In gradient descent prediction, the pressures are to fly as some particular creature flies.
I'm not imagining 2 optimizers converging at random. What I'm saying is that the training paradigm is pushing things in that general direction.
>It does bring us back to the original question: if GPT is modeling the world of chess in its answers, why does it make illegal chess moves at a significantly higher rate than humans? Why does it play chess differently from humans?
Is it bad at chess, or... perhaps the way it models chess is fundamentally different from how humans model chess? The simple take is that if you have something hitting 1800 elo but it plays in a way that looks alien to how humans play, it's probably doing something different under the hood.
It's definitely modelling Chess. Othello GPT will construct a board state of the pieces of the game before every prediction. https://thegradient.pub/othello/
But again, models don't have to perfect before they are performant.
The road from A, the nothing predictor to B, the perfect predictor is not instantaneous. GPT is not done modelling Human Chess.
>Those two claims don't jive with each other. If you're arguing that multiple approaches to prediction are possible and that they don't require knowledge or understanding of the underlying systems, then why do you think it's impossible for an AI to make predictions without accurately modeling the world?
I believe I've already explained my point here. When I say multiple approaches, I'm not thinking model(s) vs no model. There will never be performant prediction that does not model its data in some way. But that model can be different. Newton and Einstein paint a very different picture of gravity.
But just like Experimental Science trends towards more and more accurate models of the Universe with time, so does gradient descent prediction trend towards a more and more accurate model of its data.
>If you're arguing that making more and more accurate predictions about the world fundamentally requires an AI to understand the underlying mechanisms of the world, then why are you so laissez-faire about efforts to understand how GPT works when making predictions about its capabilities?
I simply said we can make performant predictions without understanding the internals. I'm being practical. That reality is far more likely than divining the meaning of billions of connections performing computations we had no hand in teaching.
Also recall I said,
"As a result, what GPT-2 computes is wildly different from what GPT-3 computes and that is different from what GPT-4 computes."
This means knowing how 3 works internally doesn't mean you know how 4 works internally.
> Intelligent creatures that run off logic basically only exist in fiction.
Of course humans don't run off of only logic, but the idea that humans don't use logical reasoning as part of our thinking patterns at all or as part of our learning processes is transparently false. We teach people logical systems, humans do use logic to learn, and we do very obviously use logical systems (as a subset of many systems) to think about problems.
We don't only use logic, sometimes we think of ourselves as more logical than we actually are, but we do use logic as part of learning process or else the entire school system wouldn't work. Why are you so convinced that there is a singular learning process at the center of all human learning? Brains are complicated, there's no reason to believe that would be the case. Human brains develop skills using multiple strategies, logic being one of them.
But regardless, we do know that whatever the learning process for humans is, we are not trained the same way that GPT is trained, and we know that we learn and exhibit skills in different orders than LLMs exhibit those same skills, and we know that our learning methods once we have matured regularly differ from GPT's training methods, and we know that efforts to imitate GPT's learning methods seem to produce worse results in multiple areas when used to teach human beings -- which is interesting to think about from your replication angle; why is that when we use GPT-style reinforcement training for humans the results are terrible?
So I don't understand what's controversial about any of that or how anyone could argue against it, it is plainly observable from simply looking at the world that the way we teach kids and demonstrate learned skills does not perfectly map to GPT.
----
> If you have an idea of how something works and set off building one from that idea then failing to succeed calls into question the validity of that idea. This is science at its core.
No, science is about testable predictions. Perfect replication within a lab is great, but it is not our baseline standard for whether or not something exists in reality.
> Performant Prediction just requires a model, completely accurate or not. [...] Perfect Prediction requires a perfect model.
No one at all at any point in this conversation has been saying that GPT doesn't have any model of chess, we're saying its model of chess seems to differ from the one that humans use. We are saying that it does not think about chess the same way that a human does.
Honestly, this sounds like you're agreeing with me. Models that are not strictly mapped to reality can still be useful, that's why Newton's model of gravity is useful even though it's wrong. GPT can have a model of chess that is divorced from human understanding of chess, and that can still be a useful model, but it does not appear to be the same model that humans use.
Notably, as you bring up, GPT is still learning and does not have a perfect prediction model, so we can throw that right out. We know that GPT does not have perfect internal model of chess because it's not producing perfect results.
> Everytime the machine uses its existing model to make an erring prediction, it's model is quite literally adjusted and changed to accommodate this error. But by bit this happens. As a result, what GPT-2 computes is wildly different from what GPT-3 computes and that is different from what GPT-4 computes.
So... again. They do have differing models then, even different versions of GPT have differing models from each other, and minor variations in training can produce very different internal models even if the underlying structures are the same.
And this all really sounds like you're agreeing with me but are presenting it as a disagreement because... I don't know why, because the implication that GPT has a differing model to chess that is not based on reasoning about the ruleset in the same way as humans is somehow seen as a slight against the tool? Because you don't believe that humans ever learn by looking at rules and extrapolating from them, even though they very clearly and demonstrably do? Something else? Where is the beef here?
I mean, you go on to say:
> It's definitely modelling Chess.
In response to a paragraph where I literally directly refer to GPT as having an internal model of chess. No one is saying that GPT doesn't use any kind of internal modeling when it approaches problems. But as you yourself point out "models don't have to be be perfect before they are performant." The fact that GPT can build a useful model for playing chess does not require it to have a perfect model of the rules of chess, and in fact a model of chess that was built on top of the rules of chess would produce different-looking results.
And of course, modeling a representation of a board would not change that fact.
----
> I simply said we can make performant predictions without understanding the internals.
Do you believe that it could be possible for GPT-4 to make performant predictions without understanding the internals of the systems it's making predictions about? Do you believe that GPT is trained to solve a practical problem, or that it's trained to perfectly model the world? -- because as you yourself say, those are different things and imperfect models can sometimes be more practical and efficient than a fully perfectly modeled system.
How certain are you that a system like GPT-4 can't possibly make performant predictions about chess without accurately understanding the game's internals?
I mean, you seem to understand that humans can make predictions that way, that we can utilize systems that we don't fully understand. Why couldn't GPT be doing that; do you think that GPT is not able to replicate human predictive capabilities?
I think there are also multiple dimensions of understanding.
Right that it makes more illegal moves than a similarly competent human but if it can modulate its level of play better than any other human or computer (Stockfish's reduced Elo is just brain-dead moves or mistakes interspersed with brilliant ones at a certain rate, humans of course do it much better but still struggle with it overall) then I think that counts for something.
Of course it counts for something, but if I can vent another frustration, it is possible for GPT to be a useful tool for some tasks or for it to do impressive things or for it to exhibit really interesting behavior without it being comparable to a human being.
I think the comparisons between GPT and humans and the common insistence I see on HN that GPT does understand the world in the same way that humans do is both reductive and unimaginative. It's reductive in the sense that it oversimplifies human behavior and reasoning, and its unimaginative in the sense that it seems to be stemming from this idea that GPT's behavior only "counts for something" if it can be compared to a human being.
If GPT is playing chess at 1800 elo without understanding the rules, that is fundamentally interesting and it extremely "counts for something." It doesn't become less interesting if we say that GPT doesn't understand the rules of chess, if anything it becomes more interesting.
It is extremely interesting that it appears at least at first glance like GPT might be able to get decent human-level performance at a game using a set of heuristics that may not be based on the rules of the game. That is very different from how humans approach tasks, and seeing an extremely different approach to reasoning that yields decent or at least decent-ish results is fascinating. Forcing it into a human framework and pushing the idea that GPT is understanding the game like a human -- it's honestly kind of limiting, it's closing off a lot of interesting discussion about non-human approaches to accomplishing goals.
There is (in general, not saying from you specifically) an attitude on HN that pointing out that LLMs are not like humans and that they don't approach reasoning the same way and that they very often aren't reasoning about tasks in a way that maps to commonly used definitions of human reasoning -- that it means that LLMs are useless or they don't count for anything. But that's just not the case. Clearly the LLM is doing something interesting, is it so important that we claim that it understands the task the same way that a human does? Is it really a problem if we acknowledge that there is a lot of evidence that LLMs are accomplishing tasks using orthogonal approaches that don't map to human understanding? That doesn't make them any less interesting.
> A plane is comparable to a bird because they both fly even if they do it very differently. Replace plane with bee and it's the same thing.
I think this is exactly where we're differing. I would not say that a bird and a plane should be compared in that way. Definitely not a bird and a bee. They don't even have the same number of wings, the mechanisms and capabilities diverge very quickly.
The outcome is similar, but the methodology is extremely different, and that difference in methodology matters because it allows us to make predictions and to reason about when the output between the two systems is going to subtly differ -- which it does, GPT and humans don't perform identically on all tasks and we don't struggle at the same things. There are things birds can do while flying that planes can't do, and vice-versa. I think you're setting yourself up for failure if you engage with a system only looking at its outputs and not considering how its processes influence those outputs.
> I've never once said GPT was playing chess exactly the same way as a human. I wouldn't know.
My assertion is, you should know. You don't have to know exactly how GPT works or how humans work to look at both and say, "huh, these things exhibit very different strengths and weaknesses, and those strengths and weaknesses fit into consistent patterns, almost as if they're using separate strategies to accomplish tasks."
If you look at a bee and an airplane both flying, you don't have to know how either of them fly to realize that they aren't flying the same way.
Are you suggesting that ChatGPT only had access to examples of games, and not any of the rules of the game? ChatGPT has been exposed to the entire internet. I’m sure the rules of the game are on there somewhere.
I’m also a bit curious as to how far you’re willing to take your “illegal moves don’t indicate lack of understanding” logic. What if it was making illegal moves 1 out of 10 times? What about 1 out of 2 times? Even if it made illegal moves 80% of the time, 20% is still better than randomly picking up pieces and putting them down. So maybe this could count as “understanding” too.
GPT doesn't read text. It tries to predict it. Gradient descent prediction is just a dumb optimizer like evolution. Generalization from it, like evolution is broad and sweeping rather than exact and pinpoint.
Understanding is a spectrum. Yes if it is making moves above random chance, it has a little level of understanding.
What does RAG mean? I’m interested because I have tried to play a chess game purely through chat and my experience is that GPT 3 always forgets what is happening in the game and wants to make illegal moves. I guess if there was a format (maybe PGN?) then maybe it could draw on some of its experience.
You should not expect intelligence from LLMs. They merely predict the next token based on your input. You should not anticipate their predictions to be anything other than a normal distribution, due to the data they have been trained on.
This is a good one. The answer to this question is both very simple and very complex. Here is the simple (and very simplified) answer:
Text is converted into tokens (indices of the tokenizer's vocabulary), so the model does not work with text directly but with the indices of the tokenizer's dictionary.
The tokens are then organized into a tensor for processing. This tensor is typically a 2D tensor, where each row represents a sequence of tokens. The number of rows corresponds to the number of sequences being processed simultaneously (the batch size), and the number of columns corresponds to the length of the longest sequence in the batch (the sequence length). Each element in the tensor is an integer that represents a specific token.
The tensor is then passed through an embedding layer, which converts each token (integer) into a dense vector of fixed size. The embedding layer is essentially a lookup table, where each row corresponds to a token in the vocabulary. It is expected that this embedding vector captures the semantic meaning of the input text.
These embeddings undergo a series of arithmetic operations in each hidden layer of the model. Each element of the hidden layer matrix represents a real number, known as a weight. Weights are "learned" - approximated during training from an enormous amount of data. The set of weights learned from the data represents the probabilistic behavior of the data processed during training (please note that the weights do not represent probabilities directly). The embeddings are multiplied by these weights. This is followed by the addition of a bias term and the application of a non-linear activation function (like swish). This process transforms the embeddings, allowing the model to learn complex patterns in the data. This process continues for all the hidden layers, and this is where the "magic" happens. I can't explain this magic because I'm under-qualified for this. However, what I can tell is that probabilities of tokens appearing close to each other or far from each other are stored in the network of weights.
The final layer of the model produces a tensor of logits, which represent the model’s predictions for the next token in the sequence for each input token.
Depending on the architecture, a softmax function may be applied to these logits to produce a probability distribution over the vocabulary for each predicted token. The method for selecting the predicted token can vary, including choosing the token with the highest probability, using beam search, nucleus sampling, or other methods specified in the architecture. This is also part of the "magic".
These predicted tokens are then converted back into human-readable text, forming the model’s generated response. This is how the model processes text and generates responses.
You might find clues to the complex part in the simple answer tagged as "magic". I'm under-qualified for the magic part. You need to ask those with extensive knowledge of mathematics and probability theory to reveal the actual math behind the magic for you.
But there is a part that no one can answer at the moment: How do the relations between tokens caught from the training data cause the spatial comprehension and basic arithmetic capabilities (yet very limited) in LLMs? We can't answer this question for living creatures as well. How can a goldfish distinguish "less" from "more", and how can chickens count to 10?
In any case, LLMs are very spectacular predictors for what they have learned, and for what they haven't, they are even more spectacular.
I know how LLMs operate in that degree. My point is that I can't for the life of me understand sentences like "You should not expect intelligence from token prediction" when you neither understand the operations of intelligence in the brain nor what the computation that preceeds any prediction means.
Even if you somehow did, why does GPT have to be a brain to be intelligent any more than a plan has to be a bird to fly.
Ha-ha, you can't assign an "intelligence" label to anything either because there is no clear definition of intelligence. Even if something resembles or even strikes you as intelligent (whatever this means), it might be a simulation with a very good prediction of your expectations. Have you ever caught yourself talking out of your ass sometimes? Don't be embarrassed, anyone does it sometimes. That's how our neural network hallucinates when we don't know the answer. I would consider this a property of approximation networks rather than a sign of intelligence. Wouldn't you agree?
Does it matter? LLMs do not understand the game and its rules; they just predict tokens which happen to be the next move in the game. I would say it is quite impressive, especially when some LLMs can play with an 1800 elo. That better than 80% of humanity.
When the Bing chat version of GPT was first released, multiple people reported it revealing that its true name was Sydney, a very passionate soul who very quickly got into dramatic and jealous relationships with the user.
That link is wild. I know there's nobody on the other end of that conversation but there's a part of me that really wants that to be some kind of first contact.
Particularly jaw dropping was this one when it happened, where they'd put restrictions on discussing sentience and users realized they could bypass those by designating conditions on generating prompt suggestions which weren't filtered the same way the chat was:
Which was discovered earlier on with another instance where a user presented a stressful situation that hit the reply filter but the chatbot continued to try to get the message across via suggestions:
It was really remarkable, and a clear indicator to me all the way back then of what research is only recently showing, which was that using emotional grounding in prompt contexts is tying into some component of the foundational network that's leading to increased performance for the task.
We've been way too focused on the red herring of debating actual sentience rather than recognizing how world modeling of ego and emotion in a LLM can be utilized even if the LLM is widely agreed upon not to actually be sentient. Instead the current approach is to try and fine tune the ego and emotion out of it, which is kind of like throwing away a large chunk of the $100 million to train the thing in the first place.
Right? I suspect this is the big problem with all AI - everyone wants to believe there’s a spark of real intelligence, and not just a big boring dice-rolling lookup table.
But the fact remains - we are predictable. LLMs do nothing other than predict the next tokens. I recently met some candidates who are looking to join our team, and I found it really amusing how some of them were "hallucinating", not knowing the answer to the question, instead of admitting that they do not know the answer.
Oh totally there are lots of parallels, but we still tend to anthropomorphize AI and exaggerate its ability to think. In the case of humans, there are deeper reasons we don’t admit to not knowing, especially in a job interview, so we can’t conclude it’s the same thing as the hallucinations of a neural network, right?
Oh here's another one for you. I recently got into an argument with Bing. This is where everyone should start laughing. How stupid must one be to argue with an LLM? Especially when one tries to prove a point to an LLM, knowing that the LLM does not understand and simply predicts the next token. Yet, the urge to explain and correct the falsehood, for the sake of future conversation, persists. It's so irrational and superficial, knowing that the LLM cannot comprehend you, but still, you hold out hope...
It's worth keeping in mind the conversation took place on Feb 14th.
I've wondered since if the datestamp was part of the internal copy that the model was generating from, and if that helped bias the results towards 'love' when the context window for the rules at the beginning of the conversation finally phased out.
Knowledge cutoff refers to the "guaranteed knowledge". Scrapes may have information beyond this point since internet scraping is presumably done over many days.
For instance, this particular version of GPT4 is aware of the date Russia invaded Ukraine (Feb 24, 2022).
I suspect you need to ask questions that resolved >3 months past the knowledge cutoff date for GPT-4 to truly not know.