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Comment by stevenhuang | Hacker News Reader

Here's how I understand it.

By nature of its architecture, it can only predict the next token. However, the architecture may be flexible enough that it may be able to uncover the underlying patterns within its training data in order to help it predict the next token.

https://en.m.wikipedia.org/wiki/Universal_approximation_theo....

Then suppose there exists a well defined algorithmic process for human level intelligence, of which some approximation to it appreciably resides in the training data.

Then perhaps the LLM or networks like it, have started to learn aspects of this algorithmic process, then leverage it in order to successfully predict the next token.

In other words, by forcing it to predict the next token, but giving it the flexibility to do so in any way it deems helpful, LLMs have learned higher order concepts such as abstract reasoning and "general intelligence".

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> In other words, by forcing it to predict the next token, but giving it the flexibility to do so in any way it deems helpful, LLMs have learned higher order concepts such as abstract reasoning and "general intelligence".

To my understanding there is not any evidence of Abstract Reasoning or "General Intelligence" in Large Language Models such as ChatGPT.

For example, I found a website that defines and has abstract reasoning tests it says are currently "AI Resistant"[0]:

> Resistance to AI tools (like ChatGPT) is checked each week.

> Based on the last check, it's unlikely that AI tools will be able to help answer this question

The test is a "Combining Figures" test where one sees a series of three shapes where the first two overlayed on top of each other create the final shape and a final series where one of the three shapes is missing and you have to figure out which of a set of possible shapes should go there.

It's pretty straightforward to show that you and I can pass this test, but ChatGPT 3.5[1] has low chances:

---

Please solve the following abstract reasoning puzzle.

Given a numbered set of 1 by 3 tiles represented by uppercase O's for empty spaces and uppercase X's for filled spaces such as:

1. OXO

2. XOO

3. XXO

And given a different numbered set of 1 by 3 tiles such as:

1. OOX

2. XOX

3. XOX

Now given just the first two 1 by 3 tiles of a numbered set of three:

1. XXX

2. OXO

Which of the following choices is the correct third tile following the previous sequence?

a. OXO

b. XOX

c. OOO

d. XXX

Please explain your reasoning for how you arrived at this choice.

---

In order to solve this abstract reasoning puzzle, we need to identify the pattern or rule that governs the transformation between the first two sets of 1 by 3 tiles. Let’s analyze the given sets:

Set 1:

1. OXO

2. XOO

3. XXO

Set 2:

1. OOX

2. XOX

3. XOX

Comparing both sets, we can see that the transformation occurs horizontally across the three tiles. Let’s look at each column independently:

1st column: In both sets, the first tile is “O”, so no filling pattern can be determined.

2nd column: In both sets, the second tile alternates between “X” and “O”.

3rd column: In both sets, the third tile repeats the same filling pattern as the second tile.

Now, let’s analyze the third and final set consisting of the tiles:

1. XXX 2. OXO

Based on the pattern observed in sets 1 and 2, we can deduce that the third tile must repeat the same filling pattern as the second tile. Looking at the second tile of the third set, it is “OXO”. Therefore, the correct third tile to follow the pattern would be “OXO”.

Therefore, the correct choice is:

a. OXO

---

[0] https://www.testdome.com/tests/abstract-reasoning-test/142 [1] https://huggingface.co/spaces/yuntian-deng/ChatGPT or similar free ChatGPT 3.5 interface

I would have said d. XXX. My reasoning being that in sets 1 and 2, the third tile has an X in every space where the first or second tile has an X.

Anyway, it seems to me that you are holding AI to a higher standard than humans.

Abstract reasoning abilities do not imply the ability to solve every abstract reasoning problem. Even humans cannot do that. If there is even a single abstract reasoning problem ChatGPT can solve, that means it has abstract reasoning abilities.

Similarly, general intelligence does not imply the ability to solve any problem. It means problem-solving abilities that are not restricted to a specific task or domain. ChatGPT has general intelligence because it can handle situations that it was not explicitly programmed to handle in a wide variety of contexts.

> I would have said d. XXX. My reasoning being that in sets 1 and 2, the third tile has an X in every space where the first or second tile has an X.

This is of course the correct reasoning, but ChatGPT was way off, as we'll explore shortly. It's just predicting the next likely token and while that works for a lot of things, clearly, it does not seemingly work well for abstract reasoning and logic.

While I think it's fair to say that being able to solve one Abstract Reasoning problem would indicate having Abstract Reasoning abilities to some degree, given that it's a multiple choice question there is a solid chance of just lucking into it (like with any human taking a test, even!).

As such, these tests typically have multiple questions of the same form but with different specific instances (much like the abstract Class vs the concrete Instance in Object Oriented Programming) in order to identify whether or not a conscious being does indeed have Abstract Reasoning skills.

So I will be more than happy to see ChatGPT solve several of these types of problems in a later iteration if it can, but I do think we've found a limitation of the class of models here.

Let's look at ChatGPT's reasoning for why that could be:

> 1st column: In both sets, the first tile is “O”, so no filling pattern can be determined.

False; both full sets have an X in the first column in the second row so this is completely wrong.

> 2nd column: In both sets, the second tile alternates between “X” and “O”.

False; only the second set alternates between "X" and "O" while the first set has two consecutive "O"'s in the first tile

> 3rd column: In both sets, the third tile repeats the same filling pattern as the second tile.

False; only the second set repeats the same filling pattern as the second second tile while the first set clearly is a combination of disparate tiles (I specifically chose that representation to make clear that it was an Additive rather than Subtractive problem, by the way)

All three of these observations are incorrect but stated authoritatively that many people might not even have noticed.

Of course, the "predict next token" engine doesn't KNOW that so it just keeps on predicting tokens into the wrong solution.

I believe it will do this most of the time and that shows it does not yet have Abstract Reasoning skills.

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