Not only is the inverse not generally true (as others have pointed out), their examples requires several mental leaps.

"Who is Tom Cruise's mother? [A: Mary Lee Pfeiffer]" and the reverse "Who is Mary Lee Pfeiffer's son?"

The word "mother" has no relationship to "son" in terms of the model, and so while the model might be able to infer a proximity relationship between "Tom Cruise" and "Mary Lee Pfeiffer" just because they appear in the same sentence, expecting the AI to guess that the inverse of mother is son is a bit of a stretch, especially when they're both lossy mappings, because the relationship is {mother,father} <=> {son,daughter}. If we're going to train models to make that mental leap, we'd have to put up with false results like "Tom Cruise is the daughter of Mary Lee Pfeiffer" unless the model is also supposed to infer that Tom means he can only be a son.

Pretraining could be reasonably expected to make it learn that mother/father and son/daughter are inverse relationships and Tom is usually a male name.

I'd argue that that's not an easy task in and of itself, but even if someone adds a special exception, there's still the issue that there are many other types of inverse relationship that we understand, but a machine that's just doing pattern matching can't be expected to understand. For instance "boss" and "employee". For instance "waiter" and "customer". For instance "manager" and "player" (in a football context) or "manager" and "artist" (in a music context) or "manager" and "customer" (in a bank context). And what's the inverse of "customer" now? And so on and so on...

All of this context works because we build up an extensive model of the world through the course of our lifetimes. LLM models don't do that, they pattern match based on stats.

Somebody would have to decide each of these things is important and create training data sets for each of them. But we implicitly understand so much context about the world that it's practically impossible to document everything we know in the form that a model can actually learn from.

Why can't it understand any of those? LLMs understand all kinds of context. That's all they do, actually - map context to to other context. Attention is a context-finding block, and attention is all you need.

We have both Sean Young and Sean Bean. Black swans still exists and the pretraining cannot rely on assumptions - provided if you want answers, not hallucinations.

So by extension, the question "The first letter of Oman is _", and "O is the first letter of country _" the same for humans?

This is obviously not as symmetrical as the initial problem, but yes you are expected to be able to easily answer the second after you read the first in a text. That is the concept of quite a lot of early secondary education level tests and also used when learning another language.

But again, you can only make that determination when you know that Oman is a country. LLM's don't know this as a fact, even if they're able to regurgitate a sentence that states this.

Why don't you think the word Oman maps to the vector space of countries? Have you checked?

As I and several other people pointed out last time this was posted, "A is B," in natural language, does not imply "B is A." "Is" can denote any of many different shades of relationship weaker than logical identity.

That's a fine reaction to the title alone but a very bad reaction to the abstract, where the failure they're criticizing is real and not a childish misunderstanding of the word "is".

Is the abstract misleading and the full paper is stupider than the upfront examples? If not this criticism seems like a total waste of time.

As a stylistic comment, on HN I am seeing more and more of "As I have always said..." or similar opening constructs. Concisely documenting a phenomena or affecting change requires detailed, sustained effort. Merely observing a pattern isn't sufficient to be notable.

A succinct point, ideally noting counter arguments, is most welcome. Further, if there is substantial prior discussion or relevant literature, a link is productive.

As a better version of this, I like to link my prior comments, or if I'm referring to another comment in the same thread, I like to link that one.

H in HTML & third W in WWW is meant to denote connections.

Even in strict logic, “is” can denote membership, as in, “squares are rectangles” does not entail “rectangles are squares”.

Because it is "(all)squares are (some)rectangles", so the reverse "(some)rectangles are (all)squares" is also true.

The difference is how you speak about a group term, not the meaning of the word 'to be'.

Yes, "Who is Mary Lee Pfeiffer's son?" happens to have just one answer, whereas "Who is Mary Lee Pfeiffer's child?" would have several.

I wouldn't resort to language examples, as the other comments show how this gets imprecise and lost in semantic details quickly. Instead think of a basic logic example: Consider an OR gate with inputs A and B and output X. If B=1 that means X=1. But if X=1 you can't infer that B=1, because there is an alternative (i.e. A=1,B=0). So from B=1→X=1, the inverse simply does not follow. This extends to all statements where a relation is not symmetric. Of course you can also go beyond and find cases where the relationship is not transitive or not even reflexive. There's a whole branch of language based IQ test puzzles (e.g. "all X are Y, some Y are Z" kind of stuff) that exploit this rabbit hole. Any LLM that does good on these will not jump to conclusions about reverse equalities quickly.

Except the researchers are not asking about the inverse of arbitrary logic statements.

It could have several correct answers, yes, where one should be logically deducible.

"Mary Lee Pfeiffer (A) is Tom Cruise's (B's) mother" and "Tom Cruise (B) is Mary Lee Pfeiffer's (A's) son" are two statements of the same relation.

EDIT: I mean if you'd go so far, even Tom Cruise could have multiple mothers, but that doesn't make "Mary Lee Pfeiffer is Tom Cruise's mother" a wrong statement, just because "one of ... mothers" is missing.

> "Who is Mary Lee Pfeiffer's child?" would have several.

Might have several. It only has one answer if there is only one child, which appears to be the case here. They are measuring against what they told the model, not necessarily facts mapping to the real world.

In this case, the correct would always include “Tom Cruise” even if it needed a clarifying “there might be others I have no knowledge of”.

> Might have several. It only has one answer if there is only one child, which appears to be the case here.

With context. It does have several which was probably GPs point, and she did not have only one child.

Quick google search turns out that Mary Lee Pfeiffer had 4 children:

Lee Ann DeVette (born 1959) (daughter) Marian Henry (born 1960) (daughter) Tom Cruise (born 1962) (son) Cass Mapother (born 1964) (daughter)

So saying "Who is Mary Lee Pfeiffer's child?" would have 4 possible answers (which is several) with all known context. Whereas like GP was saying "Who is Mary Lee Pfeiffer's son?" would have 1 identifying answer, Tom Cruise with the same context.

> In this case, the correct would always include “Tom Cruise” even if it needed a clarifying “there might be others I have no knowledge of”.

I agree with this by the way.

> It only has one answer if there is only one child

My understanding is she has four.

Why would it have several answer when you are asking for a singular child?

Isn't the right way to phrase that question be "Who are Mary Lee Pfeiffer's children?" to get multiple answers?

The space of possible answers is more than one is all GP is saying. Mary Lee Pfeiffer had 1 son, Tom Cruise, and 3 daughters. That's why saying Who is Mary Lee Pfeiffer's son was an identity, because it strictly identifies (or singles out) Mary's son, Tom.

Kind of a weird way to draw an analogy, but in math it's kind of like |x|=2 (the absolute value of x is 2) the answer for the value of x is -2 and 2 sure you could reply that the answer is 2 and be correct (even though you would still be missing something, because the space of possible answers includes both 2 and -2). To relay that back to Mary Lee Pfeiffer saying she has Tom Cruise as a child is correct, but the actual answer could include any 4 of her children (including Tom or one of the 3 daughters) and still be correct.

Yeah i understand that but it is logically right to reply with "Tom Cruise" or any of the girls to that question because by its structure it requires only 1 of the 4 answers since it asks for a singular child right?

Or is it like we are saying while that is logically correct, its not the actual answer and the model should reply "they have more than one child, here is the list of children" and that would be a more accurate one even though the prompt strictly asked for just 1 child?

> Yeah i understand that but it is logically right to reply with "Tom Cruise" or any of the girls to that question because by its structure it requires only 1 of the 4 answers since it asks for a singular child right?

Yes that is correct it is logically correct to reply Tom Cruise or any of the girls, and that was their point that there are four possible answers to one question.

> Why would it have several answer when you are asking for a singular child?

By the way this quote was the focus of my GP comment since I didn't quote it there.

> Or is it like we are saying while that is logically correct, its not the actual answer and the model should reply "they have more than one child, here is the list of children" and that would be a more accurate one even though the prompt strictly asked for just 1 child?

This was not his point so I feel like we are moving the goal posts a bit, so no though that could have been what's said I don't think that's what was really being said.

Your question arguably doesn't have multiple answers. There's one answer: the set of children (discounting different orders of the set).

The question "Who is her child" has multiple answers because it asks you to deliver a single answer.

But wouldn't every one of those multiple answers be the correct one in this case? Like it can say child a or child b or child c (hypothetical) and while there are mutiple answers, each of them is a logically right one for the question "Who is her child?" no? So how do we judge what is the absolute right answer to that? its ambigious when you say child

Zooming out to the original complaint that "A is B" doesn't imply "B is A" in common English, and then further -- to the goal of having an LLM predict tokens that map closely to truth/logic/helpfulness:

I don't think a person speaking plain English in most contexts should be seen as "correct" to answer the question with a non-list answer, even if the question is shaped to expect one, unless there's an established confidence that the shape of the question wasn't made in error.

If someone asked me in real life who "my child" is on stage, and I had multiple children on stage, I would first say that I had multiple children there, rather than choosing one from the set. It would be most helpful for an LLM in my position to do the same, rather than infer that [because Timmy is niam's child, niam's child ought to be Timmy when queried].

Yeah I guess that makes sense and which I was getting at. Even though logically it a non-list answer to that might be "correct" but not as helpful as returning the list answer and clarifying that there are multiple children. And I guess the later is also kind of more intelligent if we think about it even though it doesn't fully confirm to the exact prompt.

A single person would satisfy my question but it needn't be Tom Cruise.

Here's a short example from A. Karpathy in a 2024 video: https://www.youtube.com/watch?v=zjkBMFhNj_g&t=750s

I am surprised nobody links this blog post demonstrating that the paper's conclusion is not true (even for gpt3.5): https://andrewmayne.com/2023/11/14/is-the-reversal-curse-rea...

It seems like restrictions on the model talking about non famous people might have been responsible for the appearance of the models being unable to do this.

“A is B” doesn’t generally entail “B is A”.

“A square is a rectangle” does not entail “a rectangle is a square”.

Similarly, “Socrates is alive” doesn’t entail “alive is Socrates”.

Notably, they mention when context is included, LLM performance rises — ie, exactly when we include extra information that allows it to recognize what kind of information is being conveyed.

But the LLM is correct not to generalize that pattern when it doesn’t generalize — even if researchers have salient example, but ignore contrary ones (eg, square-rectangle or Socrates-alive).

"A is the B" does entail "the B is A", because "the" establishes an identity/bijection.

That completely misses the point. The point is that "Valentina Tereshkova was the first woman to travel to space" does imply "The first woman to travel to space was Valentina Tereshkova", which LLMs fail to recognise.

Right, but that could be because the fact that that implication exists is not actually as trivial as they are implying.

Does "Flargbler was blorglargh" imply "blorglargh was Flargbler"? Maybe. You need more context to know.

> You need more context to know.

Yes, which is what the model is. That's the point here, it doesn't has or accesses this context, even though it should.

It seems to me that the point was usually "these models are fundamentally not intelligent because they have this incredibly dumb failure modes" rather than "these models happen to have this interesting weakness".

Maybe the original authors didn't make that mistake, but that was certainly the case whenever it was brought up by HN commenters of the "stochastic parrot" persuasion.

I remember seeing this popping up in discussions the first time, but never noticed any resolution (other than to train both sides). Has SOTA advanced?

No, it's just that no one really cares. It doesn't seem to cause identifiable faults in model reasoning in the real world.

It could be fun to try to make the model pre-learn a "reversal prior" that would cause a greater degree of generalization there, but I'm yet to see a published result like this. Let alone one that would demonstrate such a prior to be useful.

Yes, because they can't reason. This is well known, and should be completely unsurprising. LLMs don't "learn" anything except that some token is statistically likely to be followed by some other token.

cool assertion from 2023

Discussed at the time:

LLMs trained on “A is B” fail to learn “B is A” - https://news.ycombinator.com/item?id=37621999 - Sept 2023 (158 comments)

The premise here is false. AI does not learn. It is a word guessing machine. I know that some of this is the semantics of how we describe these analogs but pretending that an LLM can learn does not advance the topic.

(2023)