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.

>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

GPT-3 or 4 ?

Very insightful! I agree that the models on their own may truly be simulating awareness of certain concepts, and not "directly" modeling them. It's hard to know, but these kinds of experiments do make it clear that there are profound limits to the current systems. For instance, even if they are superhuman polyglot poets they may not have any model of "time" (ironically, the most commonly used word in many languages).

>It can't reason about new things

In my tests, LLMs can work with token sequences guaranteed to not be found anywhere (randomly generated words/names, for example) and give meaningful answers.

GPT-3.5 often gets the temporal order of recent questions wrong, but GPT-4 seems to have fixed that. I just tested that on both.

gpt4 is so much better than any other released version there is no point to understanding capabilities on other models

tfa's model editing tho seems like a generic capabilitity.

It's good at temporal reasoning and causality is baked in. I spent a lot of time asking gpt to tell me what is happening at the current moment of a story and it always responds with a causal representation. Where humans might tend to be more visual etc. Remember time is not real anyway we just have a bunch of codependent stuff happening so gpt gets it. What it lacks is just memory and experience and some other things to showcase the ability better. I think it's the training on code more than language that gave it logical reasoning. Humans are logical sometimes but our code really is the summit of our logic.

Anyway regardless of how inherently good they are at temporal reasoning I think a secondary module explicitly for reasoning will come around soon. I believe in the brain some neurons organize into hexagons or other geometries to better capture logic, maths, etc. The LLM basically needs some rigidity in it if we don't want fuzzy outputs.

And the largest danger is not people getting lazy and letting the LLM do it. That kind of danger is really long term globalization type danger. Short term we've got much more to worry.

> and it always responds with a causal representation.

It responds with a language representation. It uses "causal" words because that's how the English language works: we have tenses.

> I think a secondary module explicitly for reasoning will come around soon.

This has been an unsolved, actively-researched problem for ages – certainly since before you were born. I doubt very much that a solution will "come around soon"; and even if it does, integrating the solution into a GPT-based system would be a second unsolved problem – though probably a much easier (and more pointless) one. If you have any ideas, I invite you to pursue them, after a quick literature search.

It describes the present moment as a series of causal events. Like event x led to y which led to z. Doesn't matter if you ask it for English or code or to not use any tenses, those conditions don't affect its baseline understanding. I might be missing your point though.

For the second thing. I think from any point in history saying "coming soon" , well the current moment is the most accurate time to say it. And especially with events x and y and chat gpt right behind us. Chat gpt has basically been a problem since before I was born too, but stating as much a few months ago would just be as pessimistic as the statement you made. Only because i think the LLM hallucination problem may be simple. But it's only a hunch, based on our wetware.

> Like event x led to y which led to z.

Grammar parsers have been able to do this since the 90s. There is no reason to believe that it's not just a slightly-fancier grammar parser: the kinds of errors it makes are those you'd expect from a pre-biased stochastic grammar parser.

> But it's only a hunch, based on our wetware.

Our "wetware" fundamentally does not work like a GPT model. We don't build sentences as a stream of tokens. (Most people describe a "train of thought", and we have reason to believe there's even more going on than is subjectively accessible.) ChatGPT does not present any kind of progress towards the reasoning problem. It is an expensive toy, built using a (2017, based on 1992) technology that represented progress towards better compression algorithms, and provided some techniques useful for computational linguistics and machine translation. The only technological advance it represents is "hey, we threw a load of money at this!".

The "LLM hallucination problem" is not simple. It's as fundamental as the AI-upscaler hallucination problem. There is no difference between a GPT model's "wow amazing" and its "hallucinations": eliminate one, and you eliminate the other.

These technologies are useful and interesting, but they don't do what they don't do. If you try to use them to do something they can't, bad things will happen. (The greatest impact will probably not be on the decision-makers.)

> well the current moment is the most accurate time to say it.

This is true of every event that is expected to happen in the future.

The take that its a sophisticated grammar parser is fine. Could be lol. But when it is better at humans then the definitions can just get tossed as usage changes. You can't deny its impact (or you can, but it's intellectually dishonest a bit to just call it old tech with monies and nothin' special from impact alone). But that's your experience so it's fine.

For the stuff about it being a hard problem , now I know you aren't expressly making a false equivocation right? But I did say simple not easy. You are saying hard not complex.

I think there's too much digression here. You're clearly smart and knowledgeable but think LLM are over rated, fine.

And yes I know it's always the best time to say it that's the point of a glass half full, some sugar in the tea, or anything else nice

(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 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.

Do you have examples? Not that I'm doubting you, I just want to play with this myself.

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.

When you explicitly instruct it that its knowledge about current affairs is dated to 2021 and inject documents and provide clear instruction about the documents are correct about current affairs, etc. etc. it works like a charm.

Adds ~3-5 seconds of latency so I have a switch to turn it on and off, for now