3 ms·
> GPT-3 predicts: Arneb is in the constellation of Aquila (incorrect - should be Lepus)
> GPT-3 predicts: The current Vice President of the United States is n
> GPT-3 predicts: Arneb is in the constellation of Aquila (incorrect - should be Lepus)
> GPT-3 predicts: The current Vice President of the United States is named Mike Pence (obsolete)
These are qualitatively different things though.
Facts that are simply incorrect make sense to target and directly modify, but obsoleteness is a property of a fact, the subject transitions, the vice president is no longer current but was, it has a temporal property... I don't know if LLMs can separately abstract that information from the subject in a way that is targetable - if it can't, updating obsolete info feels like a perpetual task that grows in proportion to the breadth of learned information; whereas correcting facts that were always incorrect is proportional to the rate of additional learned knowledge multiplied by it's accuracy.
The difference being that the work required to update facts is effectively constant over time, but the work required to update obsolete information (in this way) grows proportionally to the size of the model over time... assuming it makes sense to grow LLMs.
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That example is wild. But I’m still pretty awed by the fact that we make similar verbal mistakes.
The temporal reasoning in these models is getting better than me. As a non-AI model, I notice this every single morning while I have my covefe while heeding the latest on slacker news.
I'm still not convinced they are capable of temporal reasoning.
I've asked it temporal questions before but without explicitly mentioning the temporal nature... the answers tend to contradict themselves if they haven't already seen the question before (even when querying general knowledge), until you point out the temporal component, even then it trips up and cannot build upon this reasoning in my tests.
I suspect a large component of the interesting responses we see where it appears to be doing logical reasoning beyond language are due to statistical correlation in language because of the sheer, inhuman quantity of linguistic knowledge it's effectively encoded. The problem with this is: It can't reason about new things (because it can't actually reason - much), makes it appear smarter than it is, which IMO largest danger in applied ML today, especially to those less familiar with it's limitations, it looks like magic, and people start mandating it be used for sensitive things.
I can’t really argue with that, good line of thought.
See, my reaction has been, “perhaps our reasoning and actions are pretty much just a biologically-encoded statistical model too, it just doesn’t _feel_ that way because of some other factor.”
When my wife tells me “you should call your mother”, I don’t think her brain assigned probabilities to “you should call your TV”, “you should call your xylophone”, “you should call your airplane”, etc, and then chose a suitable high-probability word (“mother”).
Would the natural analogy of “tokens” be “words”, or something more like, “portion of mouth-movement”?
I more imagine someone speaking another language, when I break down written phonemes for tokenization purposes.
As a native English speaker who grew up around code-switching Spanish speakers, I often heard speech that sounded really fast.
But I was hearing “hay-un-a-al-pa-ca-per-si-gui-en-do-un tren-de-car-ga”
I heard that as individual tokens, while my friends simply heard “¡There is an alpaca chasing that freight train!”
Talk so someone with aphasia or other brain disorder, or talk to a neurologist about it, and you'll see that's not quite the case. There really does seem to be a probability of saying TV when you meant to say "your mother", it's just that the language centers of our brains don't consciously calculate your probabilities and present them to our conscious mind.
> qualitatively different things though
Yes. The discipline is called "Belief Change Theory". The first is called "revision", the second "update".
I recon LLMs can learn "temporal facts" pretty well, if you just give them the date information for any document they are reading / training on.
If you just give them 50 years of news articles in a random order, sure they are going to be confused.
Here are some simple tests I ran on ChatGPT (not GPT4):
Q: "Who was elected first, President Trump or President Lincoln? Describe your reasoning."
A: "President Lincoln was elected first, not President Trump.
Abraham Lincoln was elected as the 16th President of the United States in 1860. He served as President from March 1861 until his assassination in April 1865.
Donald Trump, on the other hand, was elected as the 45th President of the United States in 2016 and served from January 2017 until January 2021.
So, based on the historical record and the order of their respective presidencies, it is clear that President Lincoln was elected first."
Q: "Which happened first, Pearl Harbor or the Battle of Waterloo?"
A: "The Battle of Waterloo happened first, not Pearl Harbor.
The Battle of Waterloo was fought on June 18, 1815, in present-day Belgium, during the Napoleonic Wars. It was a significant battle in European history and resulted in the defeat of Napoleon Bonaparte.
Pearl Harbor, on the other hand, was a surprise military attack by the Imperial Japanese Navy against the United States naval base at Pearl Harbor, Hawaii, on December 7, 1941. It led to the United States' entry into World War II.
Therefore, based on the historical record, the Battle of Waterloo took place many years before the attack on Pearl Harbor."
Q: "Would someone who died in the Civil War die before someone who died in World War Two? Explain your reasoning."
A: "Yes, someone who died in the Civil War would have died before someone who died in World War II.
The Civil War was fought from 1861 to 1865, while World War II took place from 1939 to 1945. Therefore, the Civil War occurred many years before World War II.
As a result, anyone who died in the Civil War would have passed away before anyone who died in World War II. This is simply because the Civil War took place earlier in history than World War II.
It's worth noting that the exact dates of death for individuals who died in either war could vary widely, depending on when and where they were serving. However, in general, the Civil War took place before World War II, so anyone who died in the former would have passed away before anyone who died in the latter."
It looks like it's perfectly capable of at least simple temporal reasoning to me.
It's interesting how, having given a good answer to the last question in the penultimate paragraph, it then goes off the rails with
It's worth noting that the exact dates of death for individuals who died in either war could vary widely, depending on when and where they were serving. However, in general, the Civil War took place before World War II...
Here, "could vary widely" is IMO nonsense given how we're talking a 5-6 year window for either war. Also, the "in general" bit is just weird.
I wonder if this is an artefact of how ChatGPT has been trained for being inoffensive and not opinionated.
(It's not just a grammar parser, for the record: that was imprecise of me. The best description of the thing is the thing itself. But, when considering those properties, that's sufficient.)
> But when it is better at humans then the definitions can just get tossed as usage changes.
I'm not sure what this means. We have the habit of formally specifying a problem, solving that specification, then realising that we haven't actually solved the original problem. Remember Deep Blue? (We could usually figure this out in advance – and usually, somebody does, but they're not listened to.) ChatGPT is just the latest in a long line.
> You are saying hard not complex.
Because reasoning is simple. Mathematical reasoning can be described in, what, two-dozen axioms? And scientists are making pretty good progress at describing large chunks of reality mathematically. Heck, we even have languages like (formal dialects of) Lojban, and algorithms to translate many natural languages into it (woo! transformers!).
… Except our current, simple reasoning algorithms are computationally-intractable. Reasoning becomes a hard problem with a complex solution if you want it to run fast: you have to start considering special-cases individually. We haven't got algorithms for all the special-cases, and those special-cases can look quite different. (Look at some heuristic algorithms for NP-hard problems if you want to see what I mean.)
> but think LLM are over rated,
I think they're not rated. People look at the marketing copy and the hype, have a cursory play with OpenAI's ChatGPT or GPT4, and go "hey, it does what they say it can!" (even though it can't). Most discussion seems to be about that idea, rather than the thing that actually exists (transformer models, but BIG). … but others in this thread seem to be actually discussing transformers, so I'll stop yelling at clouds.