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%.
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.
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”
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.
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.
How do you define “understand”?
I just like the analogy of a dashboard with knobs. Input text on one wide output text on the other. “Training” AI is simply letting the knobs adjust themselves based on feedback of the output. AI never “learns” it only produces output based on how the knobs are dialed in. Its not a magic box, its just a lot of settings converting data to new data.
Do you think real “understanding” is a magic process? Why would LLMs have to be “magic” in order to understand things?
Bullshit, they are prediction engines biased from their input. Saying that no one understands what they do is a gross simplification. Do you understand how encoders work? Great! Then there is a common vocabulay! Now we move on to a probability matrix given those inputs, and we get the out put token witch circles through the input layer.
gross simplification
That was the point. Not everything needs to be literal.
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.
Love this format.
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.
Just like the human race!
Aren’t you glad education is cheap or even free in some places?
I mean, it’s probably better that it doesn’t understand it and then trying to end us like Ultron 😅
No wonder the broligarchs love it so much.
Plus some of it is made up and or adjusted to make the rich and asshole sound like heroes.
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”.
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.
“Among the tested models, GPT-4 Turbo ranked highest with 46% accuracy, while Llama-3.1-8B scored the lowest at 33.6%.“
Have they tested actual SOTA models?
I don’t think I would have made too much of a difference because the state-of-the-art models still aren’t a database.
Maybe more recent models could store more information in a smaller number of parameters, but it’s probably going to come down to the size of the model.
The Only exception there is if there is indeed some pattern in modern history that the model is able to learn, but I really doubt that.
What this article really calls to light is that people tend to use these models for things that they’re not good at because it’s being marketed contrary to what it is.
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. Everything about every ‘ai solves’ article I’ve read basically boils down to the equivalent of monkeys on typewriters, only with statistical guidance. Millions of iterations to solve what is essentially an intuitive solution for a reasonably intelligent being. I passed my driving test on the first try, after maybe a couple of weeks in school. What current ai ai doing is not intelligent in the least, and hardly efficient.
A friend’s mother was a doctor. Long ago, back in the 90s, she was talking about how there was some medical certification test that non-English speakers were passing simply by noticing key words in the question and correct answer.
About as useful as driver’s licenses in some places
It would be interesting to give these scores a bit of context: what level would a random person off the street, a history undergrad and a history professor score?
I think they all would have performed significantly better with a degree of context.
Trying to use a large language model like a database is simply A misapplication of the technology.
The real question is if you gave a human an entire library of history. Would they be able to identify relevant paragraphs based on a paragraph that only contains semantic information? The answer is probably not. This is the way that we need to be using these things.
Unfortunately companies like openai really want this to be the next Google because there’s so much money to be hired by selling this is a product to businesses who don’t care to roll more efficient solutions.
I read some of it, but I find it funny because it should be a joke for the bar to be so ridiculously high for a new technology: understanding human history.
Well it has read all we know about it.
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.
Most people don’t understand history. Anything trained on that is goanna struggle too.
Humans and LLMs learn in fundamentally different ways, though.
The nuance bit is really interesting, since I feel that nuance arises from these fundamental differences.
