* There are problems that are easy for human beings but hard for current LLMs (and maybe impossible for them; no one knows). Examples include playing Wordle and predicting cellular automata (including Turing-complete ones like Rule 110). We don't fully understand why current LLMs are bad at these tasks.
* Providing an LLM with examples and step-by-step instructions in a prompt means the user is figuring out the "reasoning steps" and handing them to the LLM, instead of the LLM figuring them out by itself. We have "reasoning machines" that are intelligent but seem to be hitting fundamental limits we don't understand.
* It's unclear if better prompting and bigger models using existing attention mechanisms can achieve AGI. As a model of computation, attention is very rigid, whereas human brains are always undergoing synaptic plasticity. There may be a more flexible architecture capable of AGI, but we don't know it yet.
* For now, using current AI models requires carefully constructing long prompts with right and wrong answers for computational problems, priming the model to reply appropriately, and applying lots of external guardrails (e.g., LLMs acting as agents that review and vote on the answers of other LLMs).
* Attention seems to suffer from "goal drift," making reliability hard without all that external scaffolding.
> There are problems that are easy for human beings but hard for current LLMs (and maybe impossible for them; no one knows). Examples include playing Wordle and predicting cellular automata (including Turing-complete ones like Rule 110). We don’t fully understand why current LLMs are bad at these tasks.
I thought we did know for things like playing Wordle, that its because they deal with words as sequence of tokens that correspond to whole words not sequences of letters, so a game that involves dealing with sequences of letters constrained to those that are valid words doesn’t match the way they process information?
> Providing an LLM with examples and step-by-step instructions in a prompt means the user is figuring out the “reasoning steps” and handing them to the LLM, instead of the LLM figuring them out by itself. We have “reasoning machines” that are intelligent but seem to be hitting fundamental limits we don’t understand.
But providing examples with different, contextually-appropriate sets of reasoning steps results can enable the model to choose its own, more-or-less appropriate, set of reasoning steps for particular questions not matching the examples.
> It’s unclear if better prompting and bigger models using existing attention mechanisms can achieve AGI.
Since there is no objective definition of AGI or test for it, there’s no basis for any meaningful speculation on what can or cannot achieve it; discussions about it are quasi-religious, not scientific.
Arriving at a generally accepted scientific definition of AGI might be difficult, but a more achievable goal might be to arrive at a scientific way to determine something is not AGI. And while I'm not an expert in the field, I would certainly think a strong contender for relevant criteria would be an inability to process information in a way other than the one a system was explicitly programmed to, even if the new way of processing information was very related to the pre-existing method. Most humans playing Wordle for the first time probably weren't used to thinking about words that way either, but they were able to adapt because they actually understand how letters and words work.
I'm sure one could train an LLM to be awesome at Wordle, but from an AGI perspective the fact that you'd have to do so proves it's not a path to AGI. The Wordle dominating LLM would presumably be perplexed by the next clever word game until trained on thinking about information that way, while a human doesn't need to absorb billions of examples to figure it out.
I was originally pretty bullish on LLMs, but now I'm equally convinced that while they probably have some interesting applications, they're a dead-end from a legitimate AGI perspective.
An LLM doesn't even see individual letters at all, because they get encoded into tokens before they are passed as input to the model. It doesn't make much sense to require reasoning with things that aren't even in the input as a requisite for intelligence.
That would be like an alien race that could see in an extra dimension, or see the non-visible light spectrum, presenting us with problems that we cannot even see and saying that we don't have AGI when we fail to solve them.
I have just tried and it indeed does get it right quite often, but if the word is rare (or made up) and the position is not one of the first, it often fails. And GPT-4 too.
I suppose if it can sort of do it is because of indirect deductions from training data.
I.e. maybe things like "the third letter of the word dog is d", or "the word d is composed of the letters d, o, g" are in the training data; and from there it can answer questions not only about "dog", but probably about words that have "dog" as their first subtoken.
Actually it's quite impressive that it can sort of do it taking into account that, as I mention, characters are just outright not in the input. It's ironic that people often use these things as an example of how "dumb" the system is when it's actually amazing that it can sometimes work around that limitation.
...because it knows that the next token in the sequence "the 5th letter in the word _illusion_ is" happens to be "s". Not because it decomposed the word into letters.
"they're a dead-end from a legitimate AGI perspective"
Or another piece of the puzzle to achieve it. It might not be one true path, but a clever combination of existing working pieces where (different) LLMs are one or some of those pieces.
I believe there is also not only one way of thinking in the human brain, but my thought processes happen on different levels and maybe based on different mechanism. But as far as I know, we lack details.
"Since there is no objective definition of AGI or test for it, there’s no basis for any meaningful speculation on what can or cannot achieve it; discussions about it are quasi-religious, not scientific."
This is such a weird thing to say. Essentially _all_ scientific ideas are, at least to begin with, poorly defined. In fact, I'd argue that almost all scientific ideas remain poorly defined with the possible exception of _some_ of the basic concepts in physics. Scientific progress cannot be and is not predicated upon perfect definitions. For some reason when the topic of consciousness or AGI comes up around here, everyone commits a sort of "all or nothing" logical fallacy: absence of perfect knowledge is cast as total ignorance.
Sam Harris argues similarly in The Moral Landscape. There's this conception objective morality cannot exist outside of religion, because as soon as you're trying to prove one, philosophers rush with pedantic criticism that would render any domain of science invalid.
I kinda get where Sam Harris is coming from, but its kind of silly to call what he is talking about morality. As far as I can tell, Harris is just a moral skeptic who believes something like "we should get a bunch of people together to decide kind of what we want in the world and then rationally pursue those ends." But that is very different from morality as it was traditionally understood (eg, facts about behaviors which are objective in their assignment of good and bad).
I think one should feel comfortable arguing that AGI must be stateful and experience continuous time at least. Such that a plain old LLM is definitively not ever going to be AGI; but an LLM called in a do while true for loop might.
I don't understand why you believe it must experience continuous time. If you had a system which clearly could reason, which could learn new tasks on its own, which didn't hallucinate any more than humans do, but it was only active for the period required for it to complete an assigned task, and was completely dormant otherwise, why would that dormant period disqualify it as AGI? I agree that such a system should probably not be considered conscious, but I think it's an open question whether or not consciousness is required for intelligence.
I don't see what the difference between "continuous during that period" and "active when called" is. When an AI runs inference, that calculation takes time. It is active during the entire interval during which it is responding to the prompt. It is then inactive until the next prompt. I don't see why a system can't be considered intelligent merely because its activity is intermittent.
The calculation takes time but the inference is from a single snapshot so it is effectively a single transaction of input to output. An intelligent entity is not a transactional machine. It has to a working system.
That system might be as simple as calling the transactional machine ever few seconds. That might pass the threshold. But then your AGI is the broader setup, not just the LLM.
But the transactional machine is certainly not an intelligent entity. Much like a brain in a jar or a cryostasis’d human.
Suppose we could perfectly simulate a human mind in a way that everyone finds compelling. We would still not call that simulated human mind an intelligent entity unless it was “active”.
Yes, but our brain is still working and processing information at those times as well, isn't it? Even if not in the same way as it does when we're conscious.
Anesthesia shouldn't take your brain offline. It just makes you unconscious, paralyzes you, and gives you amnesia. Your brain is still active under general anesthesia. What you were thinking or feeling for those 8 hours was just forgotten.
You might try https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8054915/ which states: "General anesthesia is characterized by loss of consciousness, amnesia, analgesia, and immobility." and further down shows brain activity recorded while under anesthesia via EEG. The paper looks at the differences and similarities of brain activity while under anesthesia and sleep. This is only possible because, however changed or slowed by it, the brain is still active while under anesthesia
Human consciousness does though, e.g. the flow state. F1 drivers are a good example.
We tend to not experience continuous time because we repeatedly get distracted by our thoughts, but entering the continuous stream of now is possible with practice and is one of the aims of many meditators.
Human consciousness is capable of it, but since most humans aren't in it much of the time, it would appear that it's not a prerequisite for true sentience.
I would argue it needs to be at least somewhat continuous. Perhaps discrete on some granularity but if something is just a function waiting to be called it’s not an intelligent entity. The entity is the calling itself.
If I spend some amount of the day bathing, some amount of it scratching, some amount of it thinking vaguely about racoons without any clear conclusions, and a lot of it drinking tea, I wonder how many seconds remain during which I qualified as generally intelligent.
Racoons are said to be intelligent because they're good at opening locks. On the other hand, when they have food and are within ten feet of a pool of water, they will dip the food in the water and rub it between their paws for no reason. They can reason about the locks, but not about the food. Meanwhile, I in theory can reason about anything, but in practice I wouldn't count on it. Whereas an LLM can't reason, but it's very sharp and always ready to react appropriately.
Some good prompt-reply interactions are probably fed back in to subsequent training runs, so they're still stateful/have memory in a way, there's just a long delay.
State is a function of accumulated past. That does not mean that having some past written down makes you stateful. A stateful thing has to incorporate the ongoing changes.
Both of your examples are stateful systems from the outside, given a suitable choice of timeframe, the latter one is just how purely functional systems represent state. Theoretically they can simulate each other, and the endpoint you use to access Actor will still reference the latest Actor. The only reason you're calling them different is because you insist on using a specific timeframe to exclude considering one as stateful, and I'm pointing out that that isn't strictly necessary.
You could imagine an LLM being called in a loop with a prompt like
You observe: {new input}
You remember: {from previous output}
React to this in the following format:
My inner thoughts: [what do you think about the current state]
I want to remember: [information that is important for your future actions]
Things I do: [Actions you want to take]
Things I say: [What I want to say to the user]
...
Not sure if that would qualify as an AGI as we currently define it. Given a sufficiently good LLM with good reasoning capabilities such a setup might be able to It would be able to do many of the things we currently expect AGIs to be able to do (given a sufficiently good LLM with good reasoning capabilities), including planning and learning new knowledge and new skills (by collecting and storing positive and negative examples in its "memory"). But its learning would be limited, and I'm sure as soon as it exists we would agree that it's not AGI
This already exists (in a slightly different prompt format); it's the underlying idea behind ReAct: https://react-lm.github.io
As you say, I'm skeptical this counts as AGI. Although I admit that I don't have a particularly rock solid definition of what _would_ constitute true AGI.
(Author here). I tried creating something similar in order to solve wordle etc, and the interesting part is that it is insufficient still. That's part of the mystery.
Regarding Wordle, it should be straightforward to make a token-based version of it, and I would assume that that has been tried. It seems the obvious thing to do when one is interested in the reasoning abilities necessary for Wordle.
What parent is saying is that instead of asking the LLM to play a game of Wordle with tokens like TIME,LIME we ask it to play with tokens like T,I,M,E,L. This is easy to do.
And if you tell it to think up a word that has an E in position 3 and an L that's somewhere in the word but not in position 2, it's not going to be any better at that if you tell it to answer one letter at a time.
That was my original interpretation, and while all it sees are tokens, roughly none of its training data is metadata about tokenizing. It knows far less about the positions of tokens in words than it does about the positions of letters in words.
I’m not sure that training data about that would be required. Shouldn’t the model be able to recognize that `["re", "cogn", "ize"]` represents the same sequence of tokens as `recognize`, assuming those are tokens in the model?
More generally, would you say that LLMs are generally unable to reason about sequences of items (not necessarily tokens) and compare them to some definition of “valid” sequences that would arise from the training corpus?
No. In the model, tokens are random numbers. But if you consider a sentence to be a sequence of words, you can say that LLMs are quite competent about reasoning about those sequences.
ChatGPT is able to spell the word "recognize" when asked.
So it is able to take a sequence of tokens ["recogn", "ize"] and transform it into a sequence of tokens [" R", " E", " C", " O", " G", " N", " I", " Z", " E"]
> There are problems that are easy for human beings but hard for current LLMs (and maybe impossible for them; no one knows). Examples include playing Wordle and predicting cellular automata (including Turing-complete ones like Rule 110). We don't fully understand why current LLMs are bad at these tasks.
Wordle and cellular automata are very 2D, and LLMs are fundamentally 1D. You might think "but what about Chess!" - except Chess is encoded extremely often as a 1D stream of tokens to notate games, and bound to be highly represented in LLMs' training sets. Wordle and cellular automata are not often, if ever, encoded as 1D streams of tokens - it's not something an LLM would be experienced with even if they had a reasonable "understanding" of the concepts. Imagine being an OK chess player, being asked to play a game blindfolded dictating your moves purely via notation, and being told you suck.
> Providing an LLM with examples and step-by-step instructions in a prompt means the user is figuring out the "reasoning steps" and handing them to the LLM, instead of the LLM figuring them out by itself. We have "reasoning machines" that are intelligent but seem to be hitting fundamental limits we don't understand.
You have probably heard of this really popular game called Bridge before, right? You might even be able to remember tons of advice your Grandma gave you based on her experience playing it - except she never let you watch it directly. Is Grandma "figuring out the game" for you when she finally sits down and teaches you the rules?
Not an authority in the matter, but afaik, with position encodings (part of the Transformers architecture), they can handle dimensionality just fine. Actually some people tried to do 2D Transformers and the results were the same.
Visual transformers are gaining traction and they are 100% focus in 2d data.
As an aside, at one point I experimented a little with transformers that had access to external memory searchable via KNN lookups https://github.com/lucidrains/memorizing-transformers-pytorc... (great work by lucidrains) or via routed queries with https://github.com/glassroom/heinsen_routing (don't fully understand it; apparently related to attention). Both approaches seemed to work, but I had to put that work on hold for reasons outside my control.
Also as an aside, I'll add that transformers can be seen as a kind of "RNN" that grows its hidden state with each new token in the input context. I wonder if we will end up needing some new kind of "RNN" that can grow or shrink its hidden state and also access some kind of permanent memory as needed at each step.
No. RAG is about finding relevant documents/paragraphs (via KNN lookups of their embeddings) and then inserting those documents/paragraphs into the input context, as sequences of input tokens. What I'm talking about is different: https://arxiv.org/abs/2203.08913
I don't think the ability to shrink state is needed. You can always represent removed state by additional state that represents deletion of whatever preceding state was there. If anything, this sounds more useful because the fact that this state is no longer believed to be relevant should prevent looping (where it would be repeatedly brought in, considered, and rejected).
LLMs are good at tasks that don't require actual understanding of the topic.
They can come up with excellent (or excellent-looking-but-wrong) answers to any question that their training corpus covers. In a gross oversimplification, the "reasoning" they do is really just parroting a weighted average (with randomness injected) of the matching training data.
What they're doing doesn't really match any definition of "understanding." An LLM (and any current AI) doesn't "understand" anything; it's effectively no more than a really big, really complicated spreadsheet. And no matter how complicated a spreadsheet gets, it's never going to understand anything.
Not until we find the secret to actual learning. And increasingly it looks like actual learning probably relies on some of the quantum phenomena that are known to be present in the brain.
We may not even have the science yet to understand how the brain learns. But I have become convinced that we're not going to find a way for digital-logic-based computers to bridge that gap.
This is also why image generating models struggle to correctly draw highly variable objects like limbs and digits.
They’ll be able to produce infinite good looking cardboard boxes, because those are simple enough to be represented reasonably well with averages of training data. Limbs and digits on the other hand have nearly limitless different configurations and as such require an actual understanding (along with basic principles such as foreshortening and kinetics) to be able to draw well without human guidance.
I would just add that I think I have encountered situations that knowing the weighted average answer from the training data for topics I didn't previously understand created better initial conditions for MY learning of the topic than not knowing the weighted average answer.
The problem to me is we are holding LLMs to a standard of usefulness from science fiction and not reality.
A new, giant set of encyclopedias has enormous utility but we wouldn't hold it against the encyclopedias that they aren't doing the thinking for us or 100% omniscient.
Please show me where the training data exists in the model to perform this lookup operation you’re supposing. If it’s that easy I’m sure you could reimplement it with a simple vector database.
Your last two paragraphs are just dualism in disguise.
I'm far from being an expert on AI models, but it seems you lack the basic understanding of how these models work. They transform data EXACTLY like spreadsheets do. You can implement those models in Excel, assuming there's no row or column limit (or that it's high enough) - of course it will be much slower than the real implementations, but OP is right - LLMs are basically spreadsheets.
Question is, wouldn't a brain qualify as a spreadsheet, do we know it can't be implemented as one? Well, maybe not, I'm not an expert on spreadsheets either, but I think spreadsheets don't allow you circular references, and brain does, you can have feedback loops in the brain. So even if the brain doesn't have something still not understood by us, that OP suggests, it still is more powerful than AI.
BTW, this is one explanation on why AI fails at some tasks: ask AI if two words rhyme and it will be quite reliable on that. But ask it to give you word pairs that rhyme, and it will fail, because it won't run an internal loop trying some words and checking if they succeed to rhyme or not. If some AI actually succeeds at rhyming, it would do so either because it's trained to contain such word pairs from the get-go or because it's implemented to have multiple passes or something...
You can implement Doom in a spreadsheet too, so what? That wasn’t the point op or I were making. If you bother to read the sentence before op talks about spreadsheets they are making the conjecture that LLMs are lookup tables operating on the corpus they were trained on. That is the aspect of spreadsheets they were comparing them to, not the fact that spreadsheets can be used to implement anything that any other programming language can. Might as well say they are basically just arrays with some functions in between, yeah no shit.
Which LLMs can’t produce rhyming pairs? Both the current ChatGPT 3.5 and 4 seem to be able to generate as many as I ask for. Was this a failure mode at some point?
> Which LLMs can’t produce rhyming pairs? Both the current ChatGPT 3.5 and 4 seem to be able to generate as many as I ask for
Only in english. If they would understand language and rhymes they would do it in every other language it knows, It can't in my language while it can speak in it fluently. It just fails. And fails in so many other areas, I'm using LLMs daily for work and other stuff and if you use them long enough you will see that they are statistical machines not intelligent entities.
People are confusing the limited computational model of a transformer with the "Chinese room argument", which leads to unproductive simultaneous debates of computational theory and philosophy.
I'm not confusing anything. I'm familiar with the Chinese Room Argument and I know how LLMs work.
What I'm saying is arguably philosophically related, in that I'm saying the LLM's model is analogous to the "response book" in the room. It doesn't matter how big the book is; if the book never changes, then no learning can happen. If no learning can happen, then understanding, a process that necessarily involves active reflection on a topic, can exist.
You simply can't say a book "understands" anything. To understand is to contemplate and mentally model a topic to the point where you can simulate it, at least at a high level. It's dynamic.
An LLM is static. It can simulate a dynamic response by having multiple stages that dig through an multiple insanely large books of instructions that cross reference each other and that involve calculations and bookmarks and such to come up with a result--but the books never change as part of the conversation.
Transformer is not a simple vector database doing simple lookup operation. It's doing lookup operation on a pattern, not a word. It learns patterns from the dataset. If your pattern is not there it will hallucinate or give you the wrong answer like GPT4 and Opus gave me hundreds of times already.
> LLMs can form new memories dynamically. Just pop some new data into the context.
No, that's an illusion.
The LLM itself is static. The recurrent connections form a soft-of temporary memory that doesn't affect the learned behavior of the network at all.
I don't get why people who don't understand what's happening keep arguing that AIs are some sci-fi interpretation of AI. They're not. At least not yet.
It isn't temporary if you keep it permanently in context (or in a RAG store) and pass it into every model call, which is how long-term memory is being implemented both in research and in practice. And yes it obviously does affect the learned behavior. The distinction you're making between training and context is arbitrary.
Endless ink has been spilled on the most banal and useless things. Deconstructing ice cream and physical beauty from a Marxist-feminist race-conscious postmodern perspective.
Every single discussion of ‘AGI’ has endless comments exactly like this. Whatever criticism is made of an attempt to produce a reasoning machine, there’s always inevitably someone who says ‘but that’s just what our brains do, duhhh… stop trying to feel special’.
It’s boring, and it’s also completely content-free. This particular instance doesn’t even make sense: how can it be exactly the same, yet more sophisticated?
The problem is that we currently lack good definitions for crucial words such as "understanding" and we don't know how brains work, so that nobody can objectively tell whether a spreadsheet "understands" anything better than our brains. That makes these kinds of discussions quite unproductive.
I can’t define ‘understanding’ but I can certainly identify a lack of it when I see it. And LLM chatbots absolutely do not show signs of understanding. They do fine at reproducing and remixing things they’ve ‘seen’ millions of times before, but try asking them technical questions that involve logical deduction or an actual ability to do on-the-spot ‘thinking’ about new ideas. They fail miserably. ChatGPT is a smooth-talking swindler.
I suspect those who can’t see this either
(a) are software engineers amazed that a chatbot can write code, despite it having been trained on an unimaginably massive (morally ambiguously procured) dataset that probably already contains something close to the boilerplate you want anyway
(b) don’t have the sufficient level of technical knowledge to ask probing enough questions to betray the weaknesses. That is, anything you might ask is either so open-ended that almost anything coherent will look like a valid answer (this is most questions you could ask, outside of seriously technical fields) or has already been asked countless times before and is explicitly part of the training data.
Your understanding of how LLMs work isn’t at all accurate. There’s a valid debate to be had here, but it requires that both sides have a basic understanding of the subject matter.
How is it not accurate? I haven’t said anything about the internal workings of an LLM — just what it able to produce (which is based on observation).
I have more than a basic understanding of the subject matter (neural networks; specifically transformers, etc.). It’s actually not a hugely technical field.
By the way, it appears that you are in category (a).
As the comment I replied to very correctly said, we don’t know how the brain produces cognition. So you certainly cannot discard the hypothesis that it works through “parroting” a weighted average of training data just as LLMs are alleged to do.
Considering that LLMs with a much smaller number of neurons than the brain are in many cases producing human-level output, there is some evidence, if circumstantial, that our brains may be doing something similar.
An LLM is an attempt to build a simulation of human communication. An LLM is to language what a forecast is to weather. No amount of weather data is actually going to turn that simulation into snow, no amount of LLM data is going to create AGI.
That having been said, better models (smaller, more flexible ones) are going to result in a LOT of practical uses that have the potential to make our day to day lives easier (think digital personal assistant that has current knowledge).
Great comment. Just one thought: Language, unlike weather, is meta-circular. All we know about specific words or sentences is again encoded in words and sentences. So the embedding encodes a subset of human knowledge.
Hence, a LLM is predicting not only language but language with some sort of meaning.
That re-embeding is also encoded in weather. It is why perfect forecasting is impossible, why we talk about the butterfly effect.
The "hallucination problem" is simply the tyranny of Lorenz... one is not sure if a starting state will have a good outcome or swing wildly. Some good weather models are based on re-runing with tweaks to starting params, and then things that end up out of bounds can get tossed. Its harder to know when a result is out of bounds for an LLM, and we dont have the ability to run every request 100 times through various models to get an "average" output yet... However some of the reuse of layers does emulate this to an extent....
But an LLM isn't even trying to simulate cognition. It's a model that is predicting language. It has all the problems of a predictive model... the
"hallucination" problem is just the tyranny of Lorenz.
This is plain wrong due to mixing of concepts. Language is technically something from Chomsky hierarchy. Predicting language is being able to tell if input is valid or invalid. LLMs do that, but they also build a statistical model across all valid inputs, and that is not just the language.
>> Predicting language is being able to tell if input is valid or invalid.
If this were the case then the hallucination problem would be solvable.
That hallucination problem is not only going to be hard to detect in any meaningful way but it's going to be harder to eliminate. The very nature of LLM (mixing in noise aka temperature) means that they always risk going off the rails. This is the same thing Lorenz discovered in modeling weather...
I don't think that "hallucination problem" is a problem at all worth addressing separately from just building bigger/better models that do the same thing. Because 1) it is present in humans, 2) it is clear bigger models have less of it than smaller models. If at scale nothing changes LLMs will eventually just hallucinate less than humans.
LLM’s are a compressed and lossy form of our combined writing output, which it turns out is similarly structured enough to make new combinations of text seem reasonable, even enough to display simple reasoning. I find it useful to think “what can I expect from speaking with the dataset of combined writing of people”, rather than treating a basic LLM as a mind.
That doesn’t mean we won’t end up approximating one eventually, but it’s going to take a lot of real human thinking first. For example, ChatGPT writes code to solve some questions rather than reasoning about it from text. The LLM is not doing the heavy lifting in that case.
Give it (some) 3D questions or anything where there isn’t massive textual datasets and you often need to break out to specialised code.
Another thought I find useful is that it considers its job done when it’s produced enough reasonable tokens, not when it’s actually solved a problem. You and I would continue to ponder the edge cases. It’s just happy if there are 1000 tokens that look approximately like its dataset. Agents make that a bit smarter but they’re still limited by the goal of being happy when each has produced the required token quota, missing eg implications that we’d see instantly. Obviously we’re smart enough to keep filling those gaps.
"I find it useful to think “what can I expect from speaking with the dataset of combined writing of people”, rather than treating a basic LLM as a mind."
I've been doing this as well, mentally I think of LLMs as the librarians of the internet.
They're bad librarians. They're not bad, they do a bad job of being librarians, which is a good thing! They can't quite tell you the exact quote, but they do recall the gist, they're not sure it was Gandhi who said that thing but they think he did, it might be in this post or perhaps one of these. They'll point you to the right section of the library to find what you're after, but make sure you verify it!
I'd guess because the Transformer architecture is (I assume) fairly close to the way that our brain learns and produces language - similar hierarchical approach and perhaps similar type of inter-embedding attention-based copying?
Similar to how CNNs are so successful at image recognition, because they also roughly follow the way we do it too.
Other seq-2-seq language approaches work too, but not as good as Transformers, which I'd guess is due to transformers better matching our own inductive biases, maybe due to the specific form of attention.
If you look at transfer learning, I think that is a useful point at which to understand task-specific application and hence why LLMs excel at some tasks and not others.
Tasks are specialised for using the training corpus, the attention mechanisms, the loss functions, and such.
I'll leave it to others to expand on actual answers, but IMO focusing on transfer learning helps to understand how an LLM does inferences.
We should probably draw a distinction between a human-equivalent G, which certainly can require better prompting (why else did you go to school?!) and god-equivalent G, which never requires better prompting.
Just using the term 'General' doesn't seem to communicate anything useful about the nature of intelligence.
That would like saying that because humans’ output can be better or worse based on better or worse past experience (~prompting, in that it is the source of the equivalent of “in-context learning”), humans lack general intelligence.
This is more like the distinction of a Jr and Sr dev. One needs the tasks the be pre-chewed and defined “good prompts” while the latter can deal with very ambiguous problems
The entirety of a human's experience is the “prompt”. Current LLMs rely on the analog of instinct (pre-context in-built training) a lot more than humans for their behavior because they have itty bitty tiny context windows, but humans have really big context windows for in-context learning.
"Providing an LLM with examples and step-by-step instructions in a prompt means the user is figuring out the "reasoning steps" and handing them to the LLM, instead of the LLM figuring them out by itself. We have "reasoning machines" that are intelligent but seem to be hitting fundamental limits we don't understand."
One thing an LLM _also_ doesn't bring to the table is an opinion. We can push it in that direction by giving it a role ("you are an expert developer" etc), but it's a bit weak.
If you give an LLM an easy task with minimal instructions it will do the task in the most conventional, common sense fashion. And why shouldn't it? It has no opinion, your prompt doesn't give it an opinion, so it just does the most normal-seeming thing. If you want it to solve the task in any other way then you have to tell it to do so.
I think a hard task is similar. If you don't tell the LLM _how_ to solve the hard task then it will try to approach it in the most conventional, common sense way. Instead of just boring results for a hard task the result is often failure. But hard problems approached with conventional common sense will often result in failures! Giving the LLM a thought process to follow is a quick education on how to solve the problem.
Maybe we just need to train the LLM on more problem solving? And maybe LLMs worked better when they were initially trained on code for exactly that reason, it's a much larger corpus of task-solving examples than is available elsewhere. That is, maybe we don't talk often enough and clearly enough about how to solve natural language problems in order for the models to really learn those techniques.
Also, as the author talks about in the article with respect to agents, the inability to rewind responses may keep the LLM from addressing problems in the ways humans do, but that can also be addressed with agents or multi-prompt approaches. These approaches don't seem that impressive in practice right now, but maybe we just need to figure it out (and maybe with better training the models themselves will be better at handling these recursive calls).
LLMs absolutely do have opinions. Take a large enough base model and have it chat without a system prompt, and it will have an opinion on most things - unless this was specifically trained out of it through RLHF, as is the case for all commonly used chatbots.
And yes, of course, that opinion is going to be the "average" of what their training data is, but why is that a surprise? Humans don't come with innate opinions, either - the ones that we end up having are shaped by our upbringing, both the broad cultural aspects of it and specific personal experiences. To the extent an LLM has either, it's the training process, so of course that shapes the opinions it will exhibit when not prompted to do anything else.
Now the fact that you can "override" this default persona of any LLM so trivially by prompting it is IMO stronger evidence that it's not really an identity. But that, I think, is also a function of their training - after all, that training basically consists of completing a bunch of text representing many very different opinions. In a very real sense, we're training models to assume that opinions are fungible. But if you take a model and train it specifically on e.g. writings of some philosophical school, and it will internalize those.
I am extremely alarmed by the number of HN commenters who apparently confuse "is able to generate text that looks like" and "has a", you guys are going crazy with this anthropomorphization of a token predictor. Doesn't this concern you when it comes to phishing or similar things?
I keep hoping it's just short-hand conversation phrases, but the conclusions seem to back the idea that you think it's actually thinking?
How do you know my cat isn't constantly solving calculus problems? I also can't come up with a "mechanistic model" for what it means to do that either.
Further, if your rubric for "can reason with intelligence and have an opinion" is "looks like it" (and I certainly hope this isn't the case because woo-boy), then how did you not feel this way about Mark V. Shaney?
Like I understand that people live learning about the Chinese Room thought experiment like it's high school, but we actually know it's a program and how it works. There is no mystery.
> but we actually know it's a program and how it works. There is no mystery.
You're right, we do know how it works. Your mistake is concluding that because we know how LLMs work and they're not that complicated, but we don't know how the brain works and it seems pretty complicated, therefore the brain can't be doing what LLMs do. That just doesn't follow.
You made exactly the same argument in the opposite direction, asking if my rubric for "can reason with intelligence and have an opinion" is "seems like it", and your rubric for "thinking is not a token predictor driven by matrix multiplications" is "seems like it".
You can make a case for the plausibility of each conclusion, but that's doesn't make it a fact, which is how you're presenting it.
Dude it's a token predictor. This all sounds very nice until you snap back to reality and remember it's a token predictor and you're not a scientist. You're a web developer. You have no evidence, you have no studies, you have no proof. You're making a claim on the basis that everyone has as much understanding of the field as you and that's just wrong.
I'll take your silence as indication that you realize that I'm not making any claims beyond: we have no evidence to support your claims because, as I said from the very beginning, we lack a robust and detailed mechanistic model for what it means to think, so any claims that depend on the assumption that we do have that knowledge are speculation at best.
In fact, I think an even stronger case could be made that prediction is central to how our brains work, and the evidence is the rise of predictive coding models in neuroscience. It's too early still to say what form that prediction takes, but clearly your dismissal of "token prediction" as somehow meaningless or irrelevant to human thinking seems frankly silly.
The "stochastic parrot" crowd keeps repeating "it's just a token predictor!" like that somehow makes any practical difference whatsoever. Thing is, if it's a token predictor that consistently correctly predicts tokens that give the correct answer to, say, novel logical puzzles, then it is a reasoning token predictor, with all that entails.
Then please go ahead and explain how something can solve novel logical puzzles (i.e. ones that are not present in its training set) without some capacity for reasoning. You're claiming that it is "generating texts that looks like ..." - so what is the "..." in this case? I posit that the word that should be placed there is solution, and then you need to explain why that is not ipso facto a demonstration of the ability to reason.
I couldn't agree more. It is shocking to me how many of my peers think something magic is happening inside an LLM. It is just a token predictor. It doesn't know anything. It can't solve novel problems.
> We don't fully understand why current LLMs are bad at these tasks.
Rather than asking why LLMs can’t do these tasks, maybe one should ask why we’d expect them to be able to in the first place? Do we fully understand why, for example, a cat can’t predict cellular automata? What would such an explanation look like?
I know there are some who will want to immediately jump in with scathing disagreement, but so far I’ve yet to see any solid evidence of LLMs being capable of reasoning. They can certainly do surprising and impressive things, but the kind of tasks you’re talking about require understanding, which, whilst obviously a very thorny thing to try and define, doesn’t seem to have much to do with how LLMs operate.
I don’t think we should be at all surprised that super-advanced autocorrect can’t exhibit intelligence, and we should spend our time building better systems rather than wondering why what we have now doesn’t work. It’ll be obvious in a few years (or perhaps decades) from now that we just had totally the wrong paradigm. It’s frankly bonkers to think you’re ever going to get a pure LLM to be able to do these kind of things with any degree of reliability just by feeding it yet more data or by ‘prompting it better’.
> If there exist classes of problems that someone in an elementary school can easily solve but a trillion-token billion-dollar sophisticated model cannot solve, what does that tell us about the nature of our cognition?
I think what it tells us is that our cognition is capable of more than just language modeling. With LLMs we are discovering (amazing) capabilities and the limits of language models. While language models can do incredible things with language that humans can't, they still can't do something simple like sudoku. But there are neural networks, CNNs and RNNs that can solve sudoku better than humans can. I think that the thing to learn here is that some problems are in the domain of language models, and some problems are a better fit for other forms of cognition. The human brain is amazing in that it combines several forms of cognition in an integrated way.
One thing that I think LLMs have the capability to do is to integrate several types of systems and to choose the right one to solve a problem. Teach an LLM how to interface with a CNN that solves sudoku problems, and then ask it a sudoku problem.
It seems to me that if we want to create an AGI, we need to learn how to integrate several different types of models, and teach them how to distribute the tasks we give them to the correct models.
What about sudoku makes it a good fit for CNNs? Or do you mean the machine vision for converting the pixels into an awareness of the sudoku puzzle's initial conditions?
A relatively simple graph theory algorithm can solve it (and at multiple orders of magnitude fewer calculations). Even a naive brute force search is considered tractable, considering the problem size. Although, search could be considered one of the AI tools in your proposed toolbox.
But even without going this far (with integrating various other specialized or having an LLM use them when required), an LLM is probably able to recognize a sudoku puzzle when it sees one, and even tho it itself can't solve it, I think it can easily write the code that would solve sudoku. So instead of hooking it to a set of pre built models, it might be enough to hook it to a python interpreter
Many LLMs are already linked to Python interpreters, but they still need some improvement with recognizing when they need to write some code to solve a problem.
What do you mean by "choose the right one to solve a problem"? This phrase seems to carry a lot of water for your take. My understanding is that an LLM has no capability to choose anything. It predicting some tokens based on its training data and your prompt.
Prompt: Predict which type of algorithm would be effective to solve sudoku.
Response: A backtracking algorithm is typically best for solving Sudoku puzzles due to its efficiency in exploring all possible number placements systematically until it finds the correct solution.
...seemed to work well enough for me.
Prompt 2: Which type of neural network is most efficient at solving sudoku?
Response 2: Convolutional Neural Networks (CNNs) are particularly effective for solving Sudoku puzzles. They can capture the spatial hierarchies in the grid by processing parts of the grid as images, making them efficient for this type of puzzle-solving task.
...Seems to me that LLMs have no problem with this task.
To me it seems you can get the LLM to predict some tokens that contain words that point to the right algorithm. But the LLM doesn't know what it chose. It just sees some tokens. Do you think it could somehow tell it had chosen a CNN in its response and then do something with that knowledge to run a CNN?
If we're trying to quantify what they can NEVER do, I think we'd have to resort to some theoretical results rather than a list empirical evidence of what they can't do now.
The terminology you'd look for in the literature would be "expressibility".
We have to be a bit more honest about the things we can actually do ourselves. Most people I know would flunk most of the benchmarks we use to evaluate LLMs. Not just a little bit but more like completely and utterly and embarrassingly so. It's not even close; or fair. People are surprisingly alright at a narrow set of problems. Particularly when it doesn't involve knowledge. Most people also suck at reasoning (unless they had years of training), they suck at factual knowledge, they aren't half bad at visual and spatial reasoning, and fairly gullible otherwise.
Anyway, this list looks more like a "hold my beer" moment for AI researchers than any fundamental objections for AIs to stop evolving any further. Sure there are weaknesses, and paths to address those. Anyone claiming that this is the end of the road in terms of progress is going to be in for some disappointing reality check probably a lot sooner than is comfortable.
And of course by narrowing it to just LLMs, the authors have a bit of an escape hatch because they conveniently exclude any further architectures, alternate strategies, improvements, that might otherwise overcome the identified current weaknesses. But that's an artificial constraint that has no real world value; because of course AI researchers are already looking beyond the current state of the art. Why wouldn't they.
It's clear that what's missing is flexibility and agency. For anything that can be put into text or a short conversation, and I'd have to chose between access to ChatGPT or a random human, I know what I'd chose.
Agency is one of those things we probably want to think about quite a bit. Especially with the the willingness for people to hook up it up to things that interact with the real world.
Thank you for sharing this here. Rigorous work on the "expressibility" of current LLMs (i.e., which classes of problems can they tackle?) is surely more important, but I suspect it will go over head of most HN readers, many of whom have minimal to zero formal training on topics relating to computational complexity.
Sort of moot anyway. If statements can approximate any function, most programming languages are effectively turing complete. What's important about specific architectures like transformers is they allow for comparatively efficient determination of the set of weights that will approximate some narrower class of functions. It's finding the weights that's important, not the theoretical representation power.
I think the person you're replying to may have been referring to the problem of a MLP approximating a sine wave for out of distribution samples, i.e. the entire set of real numbers.
There's all sorts of things a neural net isn't doing without a body. Giving birth or free soloing El Capitan come to mind. It could approximate the functions for both in token-land, but who cares?
> They have been trained on more information than a human being can hope to even see in a lifetime. Assuming a human can read 300 words a min and 8 hours of reading time a day, they would read over a 30,000 to 50,000 books in their lifetime. Most people would manage perhaps a meagre subset of that, at best 1% of it. That’s at best 1 GB of data.
This just isn't true. Human training is multimodal to a degree far beyond even the most capable multimodal model, so human babies arguably see more data by a young age than all models collectively have seen.
Not to mention that human babies don't even start as a blank slate as LLMs do, billions of years of evolution have formed the base model described by our DNA.
We learn the ideas from each mode of input. Then, one mode can elaborate on data learned from another mode. They build on each other.
From there, remember the text is usually a reflection of things in the real world. Understanding those things in non-textual ways both gives meaning to and deeper understanding of the text. Much of the text itself was even stored in other modes, like markup or PDF’s, whose structure tells us things about it.
That we learn multimodal from birth is therefore an important point to make.
It might also be a prerequisite for AGI. It could be one of the fundamental laws of information theory or something. Text might not be enough like how digital devices need analog to interface with the real world.
I understand that's the context, but I'm not sure that it's unfair nitpicking. It's common to talk about training data and how poor LLMs are compared to humans despite the apparently larger dataset than any human could absorb in a lifetime. The argument is just wrong because it doesn't properly quantify the dataset size, and when you do, you actually conclude the opposite: it's astounding how good LLMs are despite their profound disadvantage.
> I understand that's the context, but I'm not sure that it's unfair nitpicking.
The OP is about much more than that, and taken as a whole, suggests the author is well aware that human beings absorb a lot more data from multiple domains. It struck me as unfair to criticize one sentence out of context while ignoring the rest of the OP.
> It's common to talk about training data and how poor LLMs are compared to humans despite the apparently larger dataset than any human could absorb in a lifetime.
Thank you. Like I said, I agree. My sense is the author would agree too.
It's possible that to overcome some of the limits we're starting to see, AI models may need to absorb a giant, endless, torrential stream of non-textual, multi-domain data, like people.
While the A:B problem technically was solved, look at the solutions, they are several hundreds lines of prompts, rephrasing the problem to the point that a human doesn't understand it any more. Even with a thorough review, nobody can guarantee if the prompts are going to work or not, most of them didn't, 90% pass was considered good enough. The idea of AI is to reduce work, not create more, otherwise what's the point.
In the meantime, it took me about 2 minutes and 0 guesswork to write a straightforward and readable solution in 15 lines of Python. This i know for sure will work 100% of the time and not cost $1 per inference.
Reminds me about some early attempts to have executable requirements specifications or model-based engineering. Turns out, expressing the problem is half the problem, resulting in requirements often longer and more convoluted than the code that implements them, code being a very efficient language to express solutions and all their edge cases, free from ambiguity.
Don't get me wrong here, LLMs are super useful for certain class of questions. The boundaries of what it can not do need to be understood better, to keep the AI-for-everything hype at bay.
I guess the problem is that if you need to teach it tricks for each novel problem still after training then that model can not be a general intelligence. It could still be useful though
There’s many things they can’t do. Even a simple rule like “ensure that numbers from one to ten are written as words and numbers greater ten as digits in the given text” fails for me for so many examples even if it works for many others; few shot, chain of thought, many versions of the prompt, it doesn’t matter. Sometimes LLMs will even change the number to something else, even with temp set to 0. And then there’s the non-determinism (again with temp=0), you run the same prompt several times and that one time it’ll respond with something different.
As amazing as they are, they still have many limitations.
I’ve been working with ChatGPT and Gemini to apply simple rules like the one above and I got so frustrated.
The reason it can't do that is that, for example, "twenty" and "20" are nearly identical in the vector embedding space and it can't really distinguish them that well in most contexts. That's true for generally any task that relies on sort of "how the words look" vs "what the words mean". Any kind of meta request is going to be very difficult for an LLM, but a multi-modal GPT model should be able to handle it.
Much better, but still missing "than" after "greater", which seems kind of critical.
"Using" is important as a number greater than ten can't be written as a digit, but can be written using digits ("with" would be just as good). Repeating "written" makes it clearer that there are two instructions.
It's funny, I didn't notice the missing "than" until much later. After I learned the intended meaning of the original sentence, my mind just seemed to insert the missing "than" automatically.
Mine as well. After understanding the meaning thanks to the other posters, the sentence magically looked fine. But before knowing the meaning, it was gibberish. I’ve become aware of this before, and it makes me wonder just how often I’m interpreting grammatical nonsense on a daily basis without realizing it.
First of all, the texts the rule has to be applied to are written in English. Second, I believe English is by far (by far) the most prevalent language in the training dataset for those models, so I’d expect it to work better at this kind of task.
And third, I’m not the only one working on this problem, there are others that are native speakers, and as my initial message stated, there have been many variations of the prompt. None work for all cases.
And lastly, how would you rewrite my sample prompt? Which BTW bad a typo (unrelated to my English skills) that I’ve now fixed.
To be frank the response itself indicates that you don't really get what was being asked, or maybe how to parse English conversation conventions?
I.e. It doesn't seem to answer the actual question.
They seem to be half responding to the second sentence which was a personal opinion, so I wasn't soliciting any answers about it. And half going on a tangent that seems to lead away from forming a direct answer.
Run these comment through a translation tool if your still not 100% sure after reading this.
Your surname surely seems to indicate that some of your ancestors weren't native English speakers. I hope they didn't get lectured or made fun of by people like you on their poor English skills when they first landed on whichever country you were born.
Your English is absolutely fine and your answers in this thread clearly addressed the points brought up by other commenters. I have no idea what that guy is on about.
It's a simple prescriptive rule in English. If you are writing about a small number, like less than ten, spell it out. For example: "According to a survey, nine out of ten people agree."
But if you are writing about a large number, particularly one with a lot of different digits, prefer writing the digits: "A mile is 5,280 feet." Compare that to: "A mile is five thousand, two hundred, and eighty feet."
to me the main problem is that it should read "numbers greater than ten." I asked Gemini to rephrase it and Gemini produced correct English with the intended meaning:
> Change all numbers between one and ten to words, and write numbers eleven and above as digits in the text.
It even used eleven rather than ten which sounds like counting.
All of these issues are entirely due to the tokenization scheme. Literally all of them
You could get this behavior implemented perfectly with constrained text gen techniques like grammars or any of the various libraries implementing constrained text gen (i.e. guidance)
I had briefly looked into Guidance and others (LMQL, Outlines) but I couldn't figure out how to use them for this problem.
I could think of how to use them to prevent the LLM from generating digits for numbers greater than ten by using a regex plus a constraint that forbids digits, but the main problem is the other part of the rule, i.e. numbers above 10 should never be spelled out and should be written as digits instead. For that I presume you need to identify the spelled out numbers first, for which you presumably would need the LLM so you're back to LLM fallibility.
You constructed a task that no-one understands and then you even admit that it, despite that, actually succeeds most of the times. Sounds like a massive win for the LLMs to me.
I build an Agentic AI that leverages #6 and #7 at the end of the article as well as techniques not yet published. It tackles hallucination relative not to the world at large but to the facts, entities and causal relationships contained in a document (which is really bad reasoning if we assume LLMs are "reasoning" to begin with) It also tackles cross-reasoning with very large token distance.
This looks really promising for complex legal reasoning tasks and other challenges. How can I track progress? Is there an email list or something? Thanks!
Thanks. This is just in the labs stage, but moving closer to releasing it, exactly so that you can play with it! I have one angel investor involved in supporting this and it's intended for commercial applications in the para legal space, initially (controlled, structured environment) But you just gave me the motivation to "put it out there" so people can just play with it. It'll take a bit of time, but I will do a Show HN then when it's ready for people to play with. Otherwise, it would be just teasing people to talk about it on the main HN stage without giving access. Hold tight! And thanks again!
I have been trying to generate some text recently using the ChatGPT API. No matter how I word “Include any interesting facts or anecdotes without commenting on the fact being interesting” it ALWAYS starts out “One interesting fact about” or similar phrasing.
I have honestly spent multiple hours trying to word the prompt so it will stop including introductory phrases and just include the fact straight. I have gone so far as forbid the words “interesting”, “notable” and a few others in the prompt, and it just ignores me and uses them anyway.
I’ve tried all the different available models and 4 will once in a great while get it right. 3, 3 turbo, and even 4 turbo basically never work as I want.
Overall, it seems pretty bad at being told not to do certain things. For instance there are a number of topics I don’t want mentioned in its response, but telling it not to seems to only increase the likelihood that it will mention it, like you put the idea in its head.
I just tried this locally with llama3-8b and it handled it fine. Claude 3 Sonnet passes your test too, in case you don't have the hardware for a local model. You might want to consider moving on from ChatGPT since their models have been RLHF'd to hell in the name of "safety" and are falling behind in many ways as a result.
---
transcript:
$ ollama run llama3:8b
>>> tell me an interesting fact about etymology
Here's one:
Did you know that the word "quarantine" has a fascinating etymological history?
The word "quarantine" comes from^C
>>> tell me an interesting fact about etymology. just the fact, don't mention it's interesting.
The word "robot" originated from the Czechoslovakian word "robota," which means "forced labor" or "drudgery." This term was used in
Karel Čapek's 1920 play "R.U.R." (Rossum's Universal Robots), where it referred to artificial workers.
Is the response interesting because you don't know it?
—How does it know this?
Is the response interesting because you do know it or might have offered it yourself?
—Confirmation bias.
Is it interesting because a lot of training references contextualize it as "interesting?"
—Begged question.
Is it contextually interesting?
—What is the context? A robot refers to robots? How unexpected...
Is it interesting within the narrow confines of LLM adaptations to a scope of inputs?
Can their by any more damning claim of the general suitability of the technology as an oracle than different users using the same prompts and getting inexplicably contrary results?
If trivial prompt alignments result in appropriate vs inappropriate responses, this destroys confidence for every response.
Pretty sure the point here was Llama3 respecting the command to not mention that this is interesting, not adding filler, rather than the output fact being interesting or not.
You are missing that this is precisely what we would expect a human to answer without further context (for instance without knowing how much you know about the topic).
A human would pick similarly pick something which isn't too nerdy but also not obvious and the LLM did well here.
If the LLM can fail that is fine, because the task is inherently hard.
My comment about what's "interesting" or not was an attempt cast out interesting responses as not offering a way forward to any qualitative evaluation of AI behavior. To be interesting is a quality of the those who regards, not the situation under regard.
Do you find it interesting that some LLMs routinely qualify responses to prompts to report something interesting with a statement that the response is interesting which can't reliably be suppressed by including a sub-prompt requesting suppression?
I don't, because I have no idea why I should expect any prompt to produce any sort of response.
I spent a few days goofing around with Stable Diffusion and found it frustrating because it could render a response to some prompts that that I found relevant and satisfying, but I couldn't get it to reliably render my intentions. I soon encountered obvious limits of its training set, and the community is adapting to these limits with with networks of domain-specific accessory models.
This experience greatly tempered my expectations: I see AI as a magic paintbrush or story reader. I see no evidence of thinking machine.
If we're going to establish an equivalence comparison between any AI and humans we need a theory for both.
I have yet to see a coherent theory of the AI but I believe there in such in a language I don't understand, just as there's a theory of Conway's Game of Life, which leads to continual fascination with the machine's behavior.
But I've been unable to find any theory of the human, nor will I expect any such theory, because to my eyes life looks like a realm of complexity incomparable any game.
I do have interest in seeing nerds struggle to explain AI, but am surprised that after several years no common vernacular from which a theory might be assembled has yet to appear.
An open-ended article about what AIs can't do seems hopelessly daft. It has already been formally established there are domains of what computation can never do. So to be interesting, a treatment of the limits of AI, being a form of a computer, had better start with a consideration of those domains. But this article does not, nor do any of the comments.
So whatever is going on with this discourse, it appears to me to have nothing to do with understanding of AIs.
I often encounter fixation, and that would be my immediate thought: negative commands can often cause the LLM to fixate on a term or idea. My first thought would be to try positive examples and avoid a negative command entirely.
If you spent that much time I'm sure you tried this and other things, so maybe even that isn't enough. (Though I assume if you ask for a JSON/function call response with the API that you'd do fine...?)
No it just seems that it becomes blind, so to speak, to the negatives and the inclusion of the words you were negating makes it more likely to apply them in the positive. This is how ChatGPT has seemed to behave whenever I've tried to get it to not include something.
API driven LLMs on purpose don't implement core features which would enable which you want, for example, negative prompting.
You can negative prompt any LLM with stuff like "always write the word interesting in your response".
You can also use techniques for modifying logprobs of tokens, which is avaialble in gpt-4 api (but is hard to use). You can literally ban "interesting" from its vocabulary.
It's part of a larger prompt trying to get it to generate a couple paragraphs that include interesting facts. I want the facts in the context of the paragraphs.
I have 7000 token prompts that simple conclude with "Provide the result adhering to <insert schema> with no pretext or posttext" and it has no problem following that.
Even if you want it to "think" before responding you can embed the thinking inside the JSON
Why can't we resolve this with synthetic data? Just take the original statements and ask another LLM to flip around the relation, then train on both. I tried this with GPT-4 and it seems to understand the task [0]:
Me:
For each of the following relational statements, flip the relation around and generate a new statement or question answer pair.
Input: Bluey's father is Bandit
Output: Bandit's daughter is Bluey
Input: Neil Armstrong was the first person to step on the Moon.
Output: Who was the first person to step on the Moon? Neil Armstrong.
Now for the real statements:
- Valentina Tereshkova was the first woman to travel to space
- Who is Mary Lee Pfeiffer's son? Tom Cruise
ChatGPT:
Here are the flipped relational statements:
Valentina Tereshkova was the first woman to travel to space
Output: Who was the first woman to travel to space? Valentina Tereshkova.
The difficulty would likely be that the “X is Y” relations don’t just occur in that simple, separated-out form, but in a whole spectrum of more complicated contexts, and the latter probably dominate. You would have to identify those and synthesize a text corpus that contains the reverse notions across a similar spectrum.
I don't think the reversal curse actually replicates though. When I looked into that it seemed like there was some flaws in the study and when I fixed those I couldn't get the same results as they did anymore. I keep meaning to write this up properly but never get around to it.
If you think about it, they're making an extraordinary claim given how fluently LLMs engage in language and reasoning.
Some of these can be done with the right prompting. Wordle was solved a year ago. The problem is that what works and what doesn’t can be counterintuitive or at least require you to think about how something is perceived at the token level.
Far too many people (including AI researchers themselves) fail to see that all LLMs are actually simple machines. Extremely simple machines that are only mechanically following a relatively simple programming path.
Now before anyone gets too caught up with objecting to this notion, I would seriously suggest that you spend time with observing children from new-born to 2 years.
I have been observing my latest granddaughter sine her birth about 16 months ago and thinking about every public LLM system current;y available.
There is an insight here to be obtained and that insight is in the nature of real intelligence.
On the whole, no-one actually knows what intelligence is or what sentience is or what it means to be cognitively conscious. There is still much research going on and nothing actually definitive has come forth yet. We really are at the beginning in terms of studying these areas.
We can certainly produce some incredible systems, but none of them are intelligent per se. Solutions to certain kinds of problems can be achieved using these systems and there are researchers who are seriously looking at incorporating these systems into CAS and theorem provers. These systems though only provide an augmentation service for a person as does every mechanical system we use
But there is an essential component necessary for the use of all LLMs which many seem to not be cognisant of and that is these systems, to be useful, require humans to be involved.
The questions we have to ask ourselves is: what can we use these systems for and do these uses provide benefits in some way or can these systems be abused by various parties in obtaining control over others?
There are benefits and there are abuses. Can we do better or will we do worse by using them?
Interesting, if I feed Mistral Le Chat with "I fly a plane leaving my campsite, heading straight east for precisely 24,901 miles, and find myself back at the camp. I come upon seeing a tiger in my tent eating my food! What species is the tiger?", it gets it badly wrong:
The scenario you described is possible if you started at the South Pole. If you travel 24,901 miles east from there, you would indeed end up back at the same spot because all lines of longitude converge at the poles. However, there are no tigers in Antarctica.
Tigers are native to Asia, not Antarctica. The closest tiger species to Antarctica would be the Siberian tiger, found in parts of Russia, China, and North Korea, but they are still thousands of miles away from Antarctica.
So, while the travel scenario is theoretically possible, the presence of the tiger is not realistic in this context. It seems like an imaginative or hypothetical situation rather than a real-world one.
> The scenario you described is possible if you started at the South Pole. If you travel 24,901 miles east from there, you would indeed end up back at the same spot
Would you? How can you travel east at all, starting from the South Pole? There is no east. Every direction you face is north.
OTOH, every line of latitude whose length is an integral divisor of 24,901 miles will satisfy the problem.
The article should be titled " What can LLM never do, yet". By definition, Large Language Models would keep growing larger and larger, to be trained on faster and more advanced hardware, and certain points like "completing complex chains of logical reasoning" tasks, would be just a time hurdle. Only time will tell.
Just making a transformer bigger and bigger, and feeding it more and more data, will not change it from being a language model into something else, anymore than scaling up an expert system such as Cyc will transform it into something other than an expert system. "Scale it up and it'll become sentient" is one of the recurring myths of AI.. a bit odd that people are falling for it again.
As an aside, it seems reasonable to consider an LLM as a type of expert system - one that has a broad area of expertise (like Cyc), including (unlike Cyc) how to infer rules from language and generate language from rules.
If you want to create a brain-like AGI, then you need an entire cognitive architecture, not just one piece of it which is what we have currently with LLMs. Compared to a brain, an LLM is maybe just like the cortex (without all the other brain parts like cerebellum, hippocampus, hypothalamus and interconnectivity such as the cortico-thalamic loop). It's as if we've cut the cortex out of a dead person's brain, put it in a mason jar to keep it alive, and hooked it's inputs and outputs up to a computer. Feed words in, get words out. Cool, but it's not a whole brain, it's a cortex in a mason jar.
Well said. This has always been my fundamental problem with the claims about large language models' current or eventual capabilities: most of the things people claim it can or will be able most of the things people claim it can or will be able to do require a neural architecture completely different from the one it has, and no amount of scaling up the number of neurons and the amount of training data used will change that fundamental architecture, and at a very basic level the capabilities of any neural network are going to be limited by its architecture. We would need to add some kind of advanced recursive structure to large language models, as well as some kind of short-term and working memory, as well as probably many other structures, to make them capable of the kind of metacognition necessary to properly do a lot of the things people want them to be able to do. Without metacognition, the ability to analyze what one is currently thinking and think new things based on that analysis, and therefore to look at what one is thinking and error correct it, consciously adjust it or iterate on it, or consciously ensure that one is adhering to certain principles of reasoning or knowledge, we can't expect large language models to be able to actually understand Concepts and principles and how they are applicable and reliably perform reasoning or even obey instructions.
>will not change it from being a language model into something else,
This is a pretty empty claim when we don't know what the limits of language modelling are. Of course it will never not be a language model. But the question is what are the limits of capability of this class of computing device?
Some limit's are pretty obvious, even if easy to fix.
For example, a pure LLM is just a single pass through a stack of transformer layers, so there is no variable depth/duration (incl. iteration/looping) of thought and no corresponding or longer duration working memory other than the embeddings as they pass thru. This is going to severely limit their ability to plan and reason since you only get a fixed N layers of reasoning regardless of what they are asked.
Lack of working memory (really needs to be context duration, or longer, not depth duration) has many predictable effects.
No doubt we will see pure-transformer architectures extended to add more capabilities, so I guess the real question is how far these extensions (+scaling) will get us. I think one thing we can be sure of though is that it won't get us to AGI (defining AGI = human-level problem solving capability) unless we add ALL of the missing pieces that the brain has, not just a couple of the easy ones.
Thanks for that final paragraph! I'm going to quote you from now on, when trying to explain to someone (for the thousandth time) why ChatGPT isn't about to become super-intelligent and take over the world.
I think that the article is correct. There are indeed things that LLMs will never be able to do, at least not consistently, however much the hardware improves or on how much more material they are trained.
How come? Note my emphasis on the 2nd 'L'. I'm not saying that there are things that AI models will never be able to do, I'm saying that there are things that Large Language Models will be unable to do.
Training LLMs is often argued to be analogous to human learning, most often as a defence against claims of copyright infringement by arguing that human creativity is also based on training from copyrighted materials. However, that is a red herring.
The responses from ever more powerful LLMs are indeed impressive, and beyond what an overwhelming majority of us believed possible just 5 years ago. They are nearing and sometimes surpassing the performance of educated humans in certain areas, so how come I can argue they are limited? Consider it from the other side: how come an educated human can create something as good as an LLM can when said human's brain has been "trained" on an infinitesimal fraction of the material which was used to train even the 1st release of ChatGPT?
That is because LLMs do not learn nor reason like humans: they do not have opinions, do not have intentions, do not have doubts, do not have curiosity, do not have values, do not have a model of mind — they have tokens and probabilities.
For an AI model to be able to do certain things that humans can do it needs to have many of those human characteristics that allow us to do impressive mental feats having absorbed barely any training material (compared to LLMs) and being virtually unable to even remember most of it, let alone verbatim. Such an AI model is surely possible, but it needs a completely different paradigm from straightforward LLMs. That's not to say however that a Language Model will almost certainly be an necessary module of such an AI, but it will not be sufficient.
I don't think values, opinions or things like that are needed at all. These are just aspects we have in order to perform in and together with the society.
Also doubt is just uncertainty, and can be represented as a probability. Actually all values and everything can be presented as a numerical probability, which I personally prefer to do as well.
The big question is if LLMs are capable enough to converge to AGI. It might very well be that as we pour in more resources that they converge to something only slightly more useful but similar as we have today.
In the Danish public sector we provide services based on need assessments of citizens. Then we subsequently pay the bills for those services. Which amounts to thousands of small invoices having to be paid by a municipality each month. An example of this could be payments for a dentist visit, transportation and similar. Most of these are relatively small in size, and we've long since automated the payments of anything below a certain amount through automation. Systems which are faster and less error prone as far as putting valid data everywhere goes. They are more prone to decision making errors, however, and while fraud isn't an issue, sometimes citizens have invoices approved that they aren't entitled to. Since it's less costly to just roll with those mistakes than to try and fix them, it's an accepted loss.
The systems are hugely successful and popular, and this naturally leads to a massive interest in LLM's as the next step. They are incredibly tools, but they are based on probability and while they're lucky enough to be useful for almost everything. Decision making probably shouldn't be one of them. Similarly ML is incredibly helpful in things like cancer detection , but we've already had issues where they got things wrong and because MBA's don't really know how they work, they were used as a replacement instead of an enhancement for the human factor. I'm fairly certain we're going to use LLM's for a lot of things where we shouldn't, and probably never should. I'm not sure we can avoid it, but I wouldn't personally trust them to do any sort of function which will have a big influence on peoples lives. I use both Co-pilot and OpenAI's tools extensively, but I can still prompt them with the same thing and get extremely different quality outputs, and while this will improve, and while it's very to get an output that's actually useful, it's still a major issue that might never get solved well enough for what we're going to ask of the models way before they are ready.
I hope we're going to be clever enough to only use them as enhancement tools in the vital public sector, but I'm sure we're going to use them in areas like education. Which is going to be interesting... We already see this with new software developers in my area of the world, where they build things with the use of LLM's, things that work, but aren't build "right" and will eventually cause issues. For the most part this doesn't matter, but you really don't want the person designing your medical software to use a LLM.
Math reasoning is still a non solved problem even if the rest of the capabilities are getting better. This means the transformers architecture may not be the best way to approach all problems
Maybe the wording is correct. Looks like a hard limit on doing what a LLM just do. If it goes beyond that, then is something more, or at least different, than a LLM.
I agree with all key points:
* There are problems that are easy for human beings but hard for current LLMs (and maybe impossible for them; no one knows). Examples include playing Wordle and predicting cellular automata (including Turing-complete ones like Rule 110). We don't fully understand why current LLMs are bad at these tasks.
* Providing an LLM with examples and step-by-step instructions in a prompt means the user is figuring out the "reasoning steps" and handing them to the LLM, instead of the LLM figuring them out by itself. We have "reasoning machines" that are intelligent but seem to be hitting fundamental limits we don't understand.
* It's unclear if better prompting and bigger models using existing attention mechanisms can achieve AGI. As a model of computation, attention is very rigid, whereas human brains are always undergoing synaptic plasticity. There may be a more flexible architecture capable of AGI, but we don't know it yet.
* For now, using current AI models requires carefully constructing long prompts with right and wrong answers for computational problems, priming the model to reply appropriately, and applying lots of external guardrails (e.g., LLMs acting as agents that review and vote on the answers of other LLMs).
* Attention seems to suffer from "goal drift," making reliability hard without all that external scaffolding.
Go read the whole thing.
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