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.

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.

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.

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.

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”.

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?

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.