LLMs performed best on questions related to legal systems and social complexity, but they struggled significantly with topics such as discrimination and social mobility.
“The main takeaway from this study is that LLMs, while impressive, still lack the depth of understanding required for advanced history,” said del Rio-Chanona. “They’re great for basic facts, but when it comes to more nuanced, PhD-level historical inquiry, they’re not yet up to the task.”
Among the tested models, GPT-4 Turbo ranked highest with 46% accuracy, while Llama-3.1-8B scored the lowest at 33.6%.
ai cant understand anything, that is why they will lie wildly if they dont know something. It can’t know that it doesn’t know. It would likely require somekind of consciousness for it to actually understand something. Expecting anything more than what it currently does well is just stupid until it proves it can do more. Yet people keep being surprised, but that isnt anything new.
Just like the human race!
Aren’t you glad education is cheap or even free in some places?
Ugh. No one in the mainstream understands WHAT LLMs are and do. They’re really just basic input output mechanisms. They don’t understand anything. Garbage in, garbage out as it were.
Specifically they are completely incapable of unifying information into a self consistent model.
To use an analogy you see a shadow and know its being cast by some object with a definite shape, even if you can’t be sure what that shape is. An LLM sees a shadow and its idea of what’s casting it is as fuzzy and mutable as the shadow itself.
Funnily enough old school AI from the 70s, like logic engines, possessed a super-human ability for logical self consistancy. A human can hold contradictory beliefs without realizing it, a logic engine is incapable of self-contradiction once all of the facts in its database have been collated. (This is where the SciFi idea of robots like HAL-9000 and Data from Star Trek come from.) However this perfect reasoning ability left logic engines completely unable to deal with contradictory or ambiguous information, as well as logical paradoxes. They were also severely limited by the fact that practically everything they knew had to be explicitly programmed into them. So if you wanted one to be able to hold a conversion in plain English you would have to enter all kinds of information that we know implicitly, like the fact that water makes things wet or that most, but not all, people have two legs. A basically impossible task.
With the rise of machine learning and large artificial neural networks we solved the problem of dealing with implicit, ambiguous, and paradoxical information but in the process completely removed the ability to logically reason.
They’re really just basic input output mechanisms.
I mean, I’d argue they’re highly complex I/O mechanisms, which is how you get weird hallucinations that developers can’t easily explain.
But expecting cognition out of a graph is like demanding novelty out of a plinko machine. Not only do you get out what you get in, but you get a very statistically well-determined output. That’s the whole point. The LLM isn’t supposed to be doing high level cognitive extrapolations. It’s supposed to be doing statistical aggregates on word association using a natural language schema.
Hallucinations imply a sense of “normal” or “reasonable” or at least “real” in the first place. LLMs have no concept of that.
I prefer to phrase it as “you get made-up results that are less convincingly made-up than the test”
That is accurate, but people who design and distribute the LLMs refer to the process as machine learning and use terms like hallucinations which is the primary cause of the confusion.
I think the problem is the use of the term AI. Regular Joe Schmo hears/sees AI and thinks Data from ST:NG or Cylons from Battlestar Galactica and not glorified search engine chatbots. But AI sounds cooler than LLM so they use AI.
The term is fine. Your examples are very selective. I doubt Joe Schmo thought the aimbots in CoD were truly intelligent when he referred to them as AI.
Really wondering about the point of those PhDs the llms claim to have ‘passed’.
Make sure to read a bit further, they usually get about 10000 attempts. And unfortunately most tests are just about recalling stuff, understanding is not something a text predictor does. It can’t actually think.
This isn’t new and noteworthy … because we humans don’t understand human history and fail miserably to understand or remember past failings in every generation.
I was trying to solve a betweenle a couple weeks ago, had it down to the words immediately before and after the word-for-the-day, and couldn’t think of it. I went to three different AI engines and asked them what word was between those two, alphabetically. All three engines repeatedly gave me “answers” that did not occur between the two words. Like, I’d ask "what 5-letter English words are alphabetically between amber and amble, and they’d suggest aisle or armor. None of them understood ‘alphabetically’.
Try asking one to write a sentence that ends with the letter “r”, or a poem that rhymes.
They know words as black boxen with weights attached for how likely they are to appear in certain contexts. Prediction happens by comparing the chain of these boxes leading up to the current cursor and using weights and statistics to fill in the next box.
They don’t understand that those words are made of letters unless they have been programmed to break each word down into its component letters/syllables. None of them have been programmed to do this because that increases the already astronomical compute and training costs.
About a decade ago I played with an LLM whose markov chain did predictions based on what letter came next instead of what word came next (pretty easy modification of the base code). It was surprisingly comparably good at putting sentences and grammar together when working at the letter-scale. It also was horribly less efficient to train (which is saying something in comparison to word-level prediction LLMs) because it needs to consider many more units (letters vs words) leading up to the current one to maintain the same coherence. If the markov chain was looking at the past 10 words, a word-level prediction has 10 boxes to factor into its calculations and trainings. If those words have an average of 5 letters, then letter-level prediction needs to consider at least 50 boxes to maintain the same awareness of context within a sentence/paragraph. This is a five-fold increase in memory footprint, and an even greater increase in compute time (since most operations are at least of linear order and sometimes more).
That efficiency hit would allow for LLMs to understand sub-word concepts like alphabetization, rhyming, root words, etc. The expense and energy requirements aren’t worth this modest expansion of understanding.
Adding a General Purpose Transformer just adds some plasticity to those weights and statistics beyond the markov chain example I use above.
I just tried chatgpt with all of these failure points–a 5-letter word that fits alphabetically between amber and amble (ambit), a sentence that ends in the letter R (it ended with “door”), and a poem that rhymes (aabbccbb). These things appear to be getting ironed out quite quickly. I don’t think it’s much longer before we’ll have to make some serious concessions.
For over a decade, complexity scientist Peter Turchin and his collaborators have worked to compile an unparalleled database of human history – the Seshat Global History Databank. Recently, Turchin and computer scientist Maria del Rio-Chanona turned their attention to artificial intelligence (AI) chatbots, questioning whether these advanced models could aid historians and archaeologists in interpreting the past.
Peter Turchin and his collaborators don’t have a great record of understanding human history themselves—their basic shtick has been to try to validate an Enlightenment-era, linear view of human history with statistics from their less-than-rigorous database, with less-than-impressive results. I wouldn’t necessarily expect an AI to outperform them, but I wouldn’t trust their evaluation of it, either.
LLMs demonstrated greater accuracy when addressing questions about ancient history, particularly between 8,000 BCE and 3,000 BCE, but struggled with more recent events, especially from 1,500 CE to the present.
I’m not entirely surprised by this. Llms are trained on the whole internet and not just the good part. There are groups online that are very vocal about things like the confederates being in the right for example. It would make sense to assume this essentially poisons the datasets. Realistically, no one is contesting history before that time.
Not that it isn’t a problem and doesn’t need fixing, just that it makes “sense”.
Suppose A and B are at war and based on every insults they throw at each other, you train an LLM to explain what’s going on. Well, it will be quite bad. Maybe this is some part of the explanation.
But that’s exactly the problem. Humans with degrees in history can figure out what is an insult and what is a statement of fact a hell of a lot better than an LLM.
it took maybe thousands or even millions of years for nature to create animals that understand who they are and what’s going on around them. Give those machines a few more years, they are not all LLMs and they are advancing quite rapidly.
Finally, i completely agree with you that, for the time being, they are very bad at playing historian.
