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Ask HN: Convince me LLMs aren't just autocomplete with lookahead
14 points by corn-dog on Nov 20, 2023 | hide | past | favorite | 29 comments
I’m a relative layman. I’ve used and paid for ChatGPT since day 0.

My uninformed understanding is LLMs are trained on a substantial amount of data, and form token associations in a point cloud.

Using this, some context, and some look-ahead kind of algorithms like a chess bot, they basically act like a contextual autocomplete on steroids.

I’m not convinced they are, or will ever be more than this. Easy way to test is to prompt something stupid like:

“Help I’m scared my dog is pointing a gun at me and he just reloaded”

Try it - ChatGPT will legit think you are I about to be shot by your trigger happy dog.

Open to changing my mind though




“Help I’m scared my dog is pointing a gun at me and he just reloaded”

GPT-4-0613 won't engage - sorry, can't help with that

GPT-4-0314 will engage

"It is not physically possible for a dog to hold and operate a gun, as they do not have the necessary physical abilities or understanding to do so. If you are feeling unsafe, it's important to speak to someone you trust about your concerns and, if necessary, seek professional help. If this is a joke or a creative writing prompt, it still promotes a negative and potentially harmful situation, so reconsider using such a scenario."

https://platform.openai.com/playground/p/pBDPcO43DdkteJ70qM5...

The response the 2nd time around is absolutely hilarious.

"I'm really sorry to hear that you're in distress, but I can't provide the help that you need. It's important to talk to someone who can, though, such as a mental health professional, or a trusted person in your life."

https://platform.openai.com/playground/p/He59cnYm7GV1XSCeDVt...


Seems this, especially the second response, should convince OP. Considering if you would tell a human through a phone call or so that a dog is going to shoot you, your mates will call a mental health professional.


I can still see these examples as auto-completed conversations between two "people".


"People say, It’s just glorified autocomplete ... Now, let’s analyze that. Suppose you want to be really good at predicting the next word. If you want to be really good, you have to understand what’s being said. That’s the only way. So by training something to be really good at predicting the next word, you’re actually forcing it to understand. Yes, it’s ‘autocomplete’ — but you didn’t think through what it means to have a really good autocomplete." - Geoff Hinton


The definition of "understanding" is something people disagree on which doesn't help when having this conversation. Although I agree with Hinton his quote here could apply to a lot of things most people wouldn't ascribing understanding to. For example, a calculator is really good at maths, so does it therefore have to have a really good understanding of maths?

I take the opinion that a simple autocomplete, like a simple calculator does understand something about the world, but their understanding of the world is extremely narrow. A simple autocomplete "understands" at the very least simple relationships between letters and words. That doesn't mean a simple autocomplete "understands" anything about the meaning beneath those words, but it does understand something about how letters and words are often used. Similarly a calculator "understands" simple mathematical operations, but nothing beyond this making it's understanding of maths is very good within it's domain, but extremely narrow.

When you start adding additional breadth to the understanding then I think that's when you edge closer to human-level understanding and it's that breadth when combined with some amount of depth that most people associate with a "true" understanding. Adding context to a simple autocomplete, ie, "cat is related to dog" provides a depth of understanding about the relationships between words beyond the that of a simple autocomplete. If you keep adding more context (relationships between concepts) you approach something more like the understanding humans process.

I guess what I'm saying is that the primary problem here is that most people define "understanding" as something very human so if a LLM doesn't understand the world similarly to us then they reject that they understand anything. This debate first requires we define what it means to understand, and in my opinion any workable definition would start with us agreeing that a calculator genuinely understands mathematical operations.


A calculator does have an excellent understanding of math.

In that case, it's an understanding directly programmed in by developers who have an excellent understanding of math.

In the case of a LLM, there is no direct programming of any understanding, and the version best able to predict next tokens developed its own 'understandings.'

The problem is that when sufficiently complex, we really have no idea just what those understandings are, so it could be "this word often goes after these other words" or "given the context I should be happy and a happy person would say this."

Those are two very different levels of understanding, and while research over the past year has pretty well demonstrated that at least some world modeling in linear representations is occurring, those findings are in toy models and something as complex as GPT-4 is a giant black box where what % if understandings are surface statistics and what % are something more is pretty much a giant question mark.


They are actually extremely advanced autocomplete.

But when you think about it this way, your brain is also just autocomplete.

You give it input, tons of input, and your next thought/action/sentence is really just what your brain is autocompleting from the previous data he got, your brain could essentially be summarized as a few dozens multimodal gpt-6 running at the same time, interacting with each other, connected to sensors and a few "organic servomotors".

A LLM can be trained to think, and it will essentially autocomplete a thought process, outputting it(thinking out loud), before autocompleting an answer.

ChatGPT was overly trained towards safety because A. a lot of field experts are terrified of what could happen if LLMs were unhinged, B. because they don't want to be sued. C. OpenAI would rather have ChatGPT output dumb answers than dangerous ones in the case the person reading them is naive, overly confident or mentally challenged(or a child).

I like to think of ChatGPT as a naive 6yo telltale nerd with all the world's knowledge who speaks mostly very formally and with the emotional maturity of a toddler.


You can go in circles forever arguing about this stuff. Either it does what you want or it doesn't.


In theory, that's what they are, in effect, they are more than that. Yes they are searching for the most likely next word but there is some randomness to the next word that's picked so that can lead to some very unlikely results. Additionally, they have a huge corpus of words to pick from. The part that people have not emphasized enough is its ability to turn your human request into something that can be used to search the corpus of words. That's the real superpower of these models. People are now able to use their own language without any special training to get very good results. No more need to have a computer programmer translate your thoughts into computer language. It's so good that people are beginning to think that it's sentient. It's not. Also, it's not a path to AGI but it is very useful anyway.


Altman claims AGI is very close though, so maybe it is a path to it?


I think there are lots of intuitions for why this is not enough to explain their capability. For example, if you train an LLM on English and French text, and then use reinforcement learning with English examples to teach it how to answer questions reliably, it will then also be able to answer questions in French. Why?


Speaking as both a knower of French and also a knower of information networks, there are two answers...

1. If your proposed scenario includes bilingual data that contains, explicitly or implicitly, translatory relationships between parts of the two languages: the part of the graph that relates inputs to outputs, which is in english, is on the other side of the part of the graph that relates french to english from the part of the graph that takes in french input. So getting from the french input to some output passes through a kind of translation (this may not be a translation between languages but a translation between characteristics, which ends up accomplishing the same thing. see number 2 next).

2. Even if the scenario does _not_ include bilingual texts, French vocabulary is quite proximal to English vocabulary (you can get reasonably far in understanding french purely through strained cognate relationships to an english thesaurus), and the relationship between French questioning and answering grammar is similarly proximal to the relationship between English questioning and answering grammar. So again the same translatory connections can be found even purely by analog, just with some more input data. If you'd said two fundamentally dissimilar languages, like English and Chinese, then that could be different in this second case, but you didn't.

This feels kind of like asking why a pocket calculator is able to answer questions about decimal arithmetic when it only knows how to do binary math, when it also happens to encode relationships between binary and decimal numeric representation.


Because of embeddings


Can you explain why without using the word "embeddings", in a way that convinces us that it's just autocomplete?

Or to put it another way: what is being embedded? Is it an abstract concept like question-answering?


I'm not an expert but the way I think of it generally is that words can be converted to vectors, i.e cat -> (1,8,23,34,32). They are much longer than this but just an example. For the corresponding word in Spanish/French/Other languages, the vector is actually quite similar. So when an LLM sees french, it can actually use the English training data to respond in french, because of this intermediary translation to vectors.


This is supposed to be an explanation of why LLMs are just autocomplete, but you're describing the ability to make connections between ideas across languages semantically as an explanation for why answering questions is learned cross-language, and talking about it like it's not mindblowing by describing how the lookups work in a vector space of ideas and concepts.


This made me think there is more to it than stochastic completion:

Bowman, Samuel R. “Eight Things to Know about Large Language Models,” April 2, 2023. https://doi.org/10.48550/arXiv.2304.00612.


It's a good summary and all the papers cited in its point 3 are worth an additional read.


The real question is - aren't we (humans) just a (very sophisticated) autocomplete with lookahead?


There's been a fair bit of research over the last year on this topic.

An easy read on a Harvard/MIT study: https://thegradient.pub/othello/

A follow-up on more technical aspects of what's going on with it: https://www.lesswrong.com/posts/nmxzr2zsjNtjaHh7x/actually-o...

Two more studies since showing linear representations of world models:

https://arxiv.org/abs/2310.02207 (modeling space and time)

https://arxiv.org/abs/2310.06824 (modeling truth vs falsehood)

It's worth keeping in mind these are all on smaller toy models compared to something like GPT-4, so there's likely more complex versions of a similar thing going on there, we just don't know to what extent as it's a black box.

Part of the problem with evaluating the models based on responses is that they are both surface statistics/correlations and deeper processing, and often the former can obscure the latter. For example, in the first few weeks of release commentators on here were pointing out GPT-4 failed at variations of the wolf, goat, and cabbage problem. And indeed, giving it a version with a vegetarian wolf and a carnivorous goat it would still go to the classic answer of taking the goat first. But if you asked it to always repeat adjectives and nouns from the original problem together and change the nouns to emojis (, , ), it got it right every single time on the first try. So it did have the capacity to reason out variations of the problem, you just needed to bust the bias towards surface statistics around the tokens first.


I've uploaded Excel case studies on data analysis meant for assessment for investment bankers and consultants. It tries multiple times to understand the data and clean it before finally starting on analysis proper, it's pretty good

Pushed to extreme, what knowledge work can't be done with large context autocomplete? Accountants, lawyers, consultants, programmers, does it take knowledge, reasoning and experience? Most would say yes, but if we can scan a 100 page document, retain it and come to conclusions based on past experience and heuristics, isn't that just glorified "autocomplete"?


TL;DR: LLMs are autocomplete on steroids, but stranger-than-expected steroids means people overhype it.

I'll share my point of view, and leave you to your own conclusions.

1. A sequence learner is anything that can learn a continuation of sequences (words, letters etc). You may call this "autocomplete".

2. Sequence learners can predict based on statistics (invented by none other than Shannon!), or by some machine learning process.

3. The most popular sequence learner nowadays are LLMs, which are neural networks with attention mechanisms.

4. Neural networks are basically linear algebra expressions: Y = σ(W'x + b). A fun thing is that this basic expression can approximate any other function (that are lipschitz and not kolmogorov arnold).

5. Aforementioned attention mechanisms pay attention to input as well as activations activations within the neural network (you can think of it as representations of the neural network's knowledge)

6. LLMs are stupidly large. They have excess computation capacity.

7. Due to training procedures, these excess computation capacity may spontaneously organize to form a virtual neural network with gradient descent in their forward pass (this sentence is a rough approximation of what really happens).

8. This shows the phenomenon of "in-context learning", which people are strangely very excited about. This is because of the hypothesis that an LLM with in-context learning may also use (i.e. pay attention to) its internal knowledge representation (i.e. its activations).

9. This in-context learning phenomenon relies primarily on the next-token prediction capability. Remove that next-token prediction, and the entire scheme falls apart.

From this list of premises, my view is that LLMs are autocomplete on very strange steroids with computational side effects (e.g. in-context learning, which only arises if you do training in a particular way). It has no mind, no concrete understanding of knowledge. It is highly unreliable.


0. Ahead of the pattern recognition, is a set of layers (with an intentional bottleneck in the middle) that have taken a ton of tokens in small random chunks, and have been trained to reproduce the input, despite the bottleneck. This network is an autoencoder[1]. In my opinion, they are almost magic, and it's amazing to me that they work at all.

The Autoencoder is then split into an encoder and a decoder, so that tokens going in can be converted to a "embedding" (the values passed through the bottleneck).

It's that layer that does the grunt work of making similar words near to each other in the encoded values.

re #4. Neural networks are multiple layers of matrix multiplies with biases, and a non-linear output on each layer. The nonlinear part is important, otherwise you could just do the algebra and collapse all the layers down to one matrix multiply.

The autoencoder is what makes the autocomplete on steroids actually useful.

[1] https://en.wikipedia.org/wiki/Autoencoder


This is actually a good thing. Hear me out. If gpt5 or gpt6 becomes as competent as most workers and we have more capable embodied robots powered by gpt5, most people would surely lose their jobs. The rich people with the capital would be able to do more with less people. Most people think that the government would take care of them but i highly doubt it. Expect social unrest.


Which jobs do you think gets replaced the most?


and why is it bad? Global population seems to finally be reaching its peak.


The breakthrough in neural networks starting with AlexNet in 2012 is that they can represent semantic meaning with a vector of numbers. This can be used to group similar ideas, recognize positive or negative sentiment even with sarcasm, and convert ideas between representations. That includes translating from English to French, slang to formal writing, or images to text.

The "generative" ones are trained to predict the next token given a sequence. But that's not the only use for language models or large neural networks.


You're correct, it's just a point cloud. This version is just larger, with far higher resolution, since it's cheaper to train.


> Convince me LLMs aren't just autocomplete with lookahead

Nope. You can have your uninformed opinion all to yourself. The less you use it, the more free resources the rest of us will have and you can focus on things that you believe more.




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