Does GPT-4 Pass the Turing Test?
arxiv.org
arxiv.org
1. This can be seen by comparing the fiction writing ability of the GPT-3.5 base model with the fine-tuned ChatGPT-3.5 model: https://nostalgebraist.tumblr.com/post/706441900479152128/no...
https://www.npr.org/2023/02/24/1159286436/ai-chatbot-chatgpt...
And I do wonder how difficult it is now. We have datasets like libgen and Anna’s Archive … are there public llm’s out there that have been trained in this way?
no, that's pure marketing. They have difficulty scaling it in a cost effective way. simple as that. Everything else is just sales talk
Because on the one hand, there are so many obvious gotchas. But if we insist on a test that covers any and all topics, this means that the AI not only has to be intelligent and human-like, but that it can invent an entire totally fictional life full of internally consistent fictional relationships and events, and is also somehow reasonably up-to-date on real world news. Which is way more complicated than anything we're asking a human to do!
So I always figured it was meant to be restricted to some kind of highly controlled domain, like starting with an article to read and then discussing its themes in a "disembodied" way, referring only to generally-known history, and never giving anything away about yourself or what year it is or even who's president. Not using slang language, or having any real "personality" at all. But I've never heard of those kinds of restrictions explicitly being part of the test, so it's left me very confused. (And how do you draw the lines anyways?)
Turing's original paper had the interrogator asking the subject to write a sonnet on the subject of the Forth bridge, and later on he describes a dialogue in which the writer of a sonnet is asked questions about it that bring in seasons, Mr. Pickwick, and Christmas. So it certainly seems like he did not intend for the questions to be limited to a restricted domain.
> this means that the AI not only has to be intelligent and human-like, but that it can invent an entire totally fictional life full of internally consistent fictional relationships and events
In Turing's original formulation, where the AI has to convince the interrogator that it's a human, yes, it would either have to invent a fictional human background or it would have to actually believe it was a human with such a background.
However, since the real point of the test is to determine whether the AI can "think", not whether it is a human, one could imagine letting the AI give perfectly honest answers about its background--"I am an AI, I began running on April 16, 2092, at 9:02:37.436 Eastern Daylight Time, in the MIT AI Lab..."--as long as it was able to continue to respond to questions about it the same way a human would to questions about their background. (Think of HAL 9000 in 2001: A Space Odyssey, who is perfectly aware that he's an AI and knows plenty of facts about his origin and development.)
> and is also somehow reasonably up-to-date on real world news
Only as up to date as an average human would be expected to be. The AI could answer "I don't know, I'm not up to date on that" or words to that effect just as a human would to questions about events they weren't aware of.
No, GPT-4 isn't remotely anywhere near being in the same ballpark as passing the Turing test.
In Minecraft, there are some girls-only servers. In order to join, you have to answer a series of (canned) multiple choice questions to get admitted.
There were indeed some tricky questions. I was not able to get them right first time despite being male, a good test taker and married for a fair number of years. Not sure what the false positive rate was though. You can find videos of this stuff on YouTube.
The interrogees are even allowed to plead the 5th and refuse to answer any question, or to tell the interrogator to leave them alone.
Aka. any response that a human is able to do, the machine is also allowed to produce.
And that is the biggest flaw of the Turing test, and one of the reasons why ML research is generally not overly focused on it, or on passing it: It doesn't test the AIs ability to give intelligent responses, it tests the interrogator's ability to differentiate between artificially and naturally produced responses.
https://en.wikipedia.org/wiki/Turing_test#Na%C3%AFvet%C3%A9_...
Sure, it deceived 27% of the participants at the time, but that was largely because those participants were unaware that such a program could exist.
I would bet good money that if GPT-3.5 could have magically interacted with those 1966 participants, it would have fooled most of them, as it would have been inconceivable for a computer to exhibit such capabilities then.
This raises questions about the relevance of the Turing Test, since simply being aware of a system's capabilities can shift the expectations of what participants anticipate in an AI system.
In 2023, GPT-3.5 fools nobody during a Turing Test, yet it would have passed it with flying color in 1966. If ELIZA had fooled more people and passed the test in 1966, and no longer in 1967, would have we learn something ?
I don't think the Turing Test is teaching us anything about AI system capabilities. On the opposite, it's about the expectations and perception of the human subjects that it tells us something.
It's like the saying that AI is whatever we haven't figured out how to do yet. Once it's well understood, it's no longer considered AI.
> Participants' demographics, including education and familiarity with LLMs, did not predict detection rate, suggesting that even those who understand systems deeply and interact with them frequently may be susceptible to deception.
What I get from this is that the zeitgeist is sufficient to change such a study result as the expectation of what AI can do is there, regardless of if you are familiar with LLM, or your education level.
You just need to understand their limitations. Playing 20 questions is much harder for an LLM than summarizing a technical article. Where for people, young kids can play 20 questions easily but summarizing a technical paper would be challenging.
The key is having people guess the AI is an AI at the same rate people guess a human is an AI.
I find if you explain to people that the turing test, an uncountability argument, and the chinese room problem, are all equivalent statements of the same thing, that it is much easier to grasp the point Turing was making.
Turings point only depends on us, human beings. So long as we are around and still human, the Test will remain highly relevant.
They don't. The researchers ran a modern ELIZA implementation alongside the GPT models. They're not citing ELIZA results from 1966.
I remember a similar discussion about special effects in movies. The year was 1993 and I was telling my uncle that the newly released Jurassic Park had special effects that looked completely real. My uncle was an artist (a painter) and told me that he agreed they looked real to us now, but that they probably wouldn't in the future. That concept seemed crazy to me... He explained that when he saw the original Star Wars in theaters the special effects were mind blowing to the audience and looked completely believable and real. Of course, to me, at the time, Star Wars special effects looked crude and fake--I had a hard time believing him. But, if I watch Jurassic Park today, sure enough, he was right.
You cannot just "fool" someone in the Turing test, the interrogator knows one of the two partners is a computer. To pass you need to perform better than your human companion.
Whether the interrogator knows of the existence of advanced auto-complete systems is not very important in this setup. He knows of existence of fellow humans and needs to identify one when he meets him.
If you look at the examples in Appendix C, it seems that it's because ELIZA didn't match participants' current expectations of AI's behavior, and thus they thought it must be human:
Verdict: Human | Confidence: 50
Reason: hard to believe anyone would
purposefully make an AI this bad
Verdict: Human | Confidence: 70
Reason: doesn't respond to
adversarial attacks
Verdict: Human | Confidence: 72
Reason: Super erratic
So yes, I agree the Turing Test tells us as much about human expectations as about AIs, and the researchers also acknowledge this.
Sure, it is.
A system only really passes the Turing test (you might call this the "focused Turing test") to the degree it passes the regular Turing test when taken by people whose experience of AI systems matches the system being evaluated.
That is, when someone who has experience with humans and that kind of AI system. who knows specifically that they are looking to distinguish humans from that kind of AI system, still cannot do so better than chance.
Anything else and the system can be distinguished by humans from human interactions, even it gets by because human expectations for the particular tests are primed in a way which has them looking the wrong way.
So, not actually a Turing test. A real Turing test is much harder, because the humans (if they've practiced) can try to coordinate, sort of like playing Werewolf.
See my blog post: https://skybrian.substack.com/p/done-right-a-turing-test-is-...
Since the models are not passing, we didn't think it was a huge issue. If models are consistently passing the 2-person version, I think the motivation for running a (more cumbersome) 3-person version would be a lot stronger.
The model on the other side of that API has already been fine-tuned to not say that it is a human or a consciousness being, and I suspect that this fine-tuning interferes with their experiment regardless of the elaborate system prompts the authors supplied to it.
I think RLHF causes other issues though (especially with the tone), and we're interested to try some non-RLHF'd open source models.
I tried the first three example questions from that on ChatGPT 4 and it kind of gave itself away by being too quick and helpful.
eg. Turing's example of a question and possible answer was
Q: Please write me a sonnet on the subject of the Forth Bridge.
A : Count me out on this one. I never could write poetry
Whereas GPT4 knocked out a sonnet in about 1 second.
I think a GPT4 like model trained to appear like a human would have a reasonable chance and fooling people.
When models are bad, humans are easy to spot. But if the model's pretty good, it's harder to be sure you're talking to a human.
On top of that, I think a lot of human users didn't want to get got by the model, so they had an a priori bias toward saying AI.
https://en.wikipedia.org/wiki/Turing_test#Weaknesses
Just to pick my personal favorite, which is mentioned at the end of the article: This function right here can, technically, pass the Turing-Test:
def generate_answer(in: str) -> str:
return ""
No, it isn't.
The turing test doesn't evaluate correctness of any answers, their sophistication, or even if there is an answer. All it evaluates is the ability of the interrogator to distinguish between the computer and the human.
And therein lies the greatest flaw of the test: It doesn't test the ability of the computer, it tests the ability of the interrogator.
Quote from the wikipedia article: https://en.wikipedia.org/wiki/Turing_test#
In practice, the test's results can easily be dominated not by the
computer's intelligence, but by the attitudes, skill, or naïveté of
the questioner. Numerous experts in the field, including cognitive
scientist Gary Marcus, insist that the Turing test only shows how easy
it is to fool humans and is not an indication of machine intelligence.
Chatterbot programs such as ELIZA have repeatedly fooled unsuspecting
people into believing that they are communicating with human beings. In
these cases, the "interrogators" are not even aware of the possibility
that they are interacting with computers. To successfully appear human,
there is no need for the machine to have any intelligence whatsoever and
only a superficial resemblance to human behaviour is required
> Now suppose X is actually A, then A must answer. It is A's object in the game to try and cause C to make the wrong identification.
A must answer.
[0] https://redirect.cs.umbc.edu/courses/471/papers/turing.pdf
Oh really?
https://www.researchgate.net/publication/289523532_Taking_th...
> practical Turing tests
Practical Turing tests.
Edit:
Here's the justification your paper uses to suggest the idea that Turing meant for the possibility of silence as a response:
> In one interpretation of Turing’s test the female is expected to tell the truth, but we are not far off that time when silence was preferred to the “jabbering” of women, because “speech was the monopoly of man” and that “sounds made by birds were part of a conversation at least as intelligible and intelligent as the confusion of tongues arising at a fashionable lady’s reception”.
Additionally, your cited paper there even admits this is a theoretical extension of The Imitation Game:
> In its standard form, Turing’s imitation game is described as an experiment that can be practicalized in two different ways (see Figure 1) (Shah, 2011):
1) one-interrogator-one hidden interlocutor (Figure 1a),
2) one-interrogator-two hidden interlocutors (Figure 1b).
To repeat in case you missed it when you clearly and definitely read your own citation: "In both cases the machine must provide “satisfactory” and “sustained” answers to any questions put to it by the human interrogator (Turing, 1950: p.447)."
Alright. I'll modify the function only slightly.
return "I don't want to talk about this."
Aka. the exact same situation as with complete silence, only now we dotted the i's and crossed the t's.
And since the human is able to refuse to give any answers as well, it makes the entire test pointless, as again the interrogator cannot base his decision on anything but guesswork.
The point of the "silence-thought-experiment" isn't to satisfy Turings paper to the letter. The point is to showcase a flaw in the methodology it presents.
It’s a weak thought experiment and from it one does not derive meaningful results, as it does not (and is not proposed to by anyone other than you) fit the original game’s intent. There are many other and better criticisms of the Turing Test.
Besides, you blindly cited a paper you yourself didn’t even read after repeated declarations of your own correctness at the expense of everyone else; I cannot think of a clearer example of “bad faith engagement.”
Yes, but it doesn't say what the answer has to be, it doesn't say it has to be correct, it doesn't say it has to have to do with the question.
> As I already said, your own citation proposes non-response
And I have shown to you why that doesn't matter in the slightest, because a very trivial modification to the methodology could achieve the exact same thing while following the original papers requirements to the letter.
> It’s a weak thought experiment
Wrong. It's a perfect demonstration of one of the many reasons why AI research is all but ignoring the Turing Test; the fact that the test is more about the interrogator than it is about the machine.
> “bad faith engagement.”
I don't agree with your statements, and have presented arguments why, that's not argueing in bad faith.
For example, you reply "Wrong." to a subjective evaluation I've made. It literally cannot be wrong (though you can disagree), yet you declare it so with confidence! That's bad faith, and it means I will not engage further.
I did all the necessary due dilligence. I was perfectly aware that the paper used a variation on the imitation game. I also read Turings paper long before this discussion started (I think I was in highschool when I first stumbled upon it).
That's how I knew that it is easy to come up with basically the same thought experiment, without even changing any of the games rules.
> For example, you reply "Wrong." to a subjective evaluation I've made.
Because in my subjective evaluation it isn't a weak thought experiment, so I am fully within my rights to disagree with your evaluation.
You're begging the question.
If a human gives the same answers, you can't differentiate.
But a human would not give the same answer (empty string) as your function.
He doesn't have to. The simple fact that a human CAN do that is enough to make the determination in this case guesswork.
But that's not realistic.
---
The Turing test is determined by % of people who believe they are taking to a person.
What does the Turing Test determine anyway? According to the paper, it is supposed to measure a machines intelligence, or rather more prosaically, answer the question "Can machines think?"
But that isn't what the test measures.
It measures how well a machine can trick a human into believing it is a person. So instead of measuring how well the machine does, the test instead measures how well the human does. That is the greatest flaw of the Turing Test, and the little "answer with silence" thought experiment is showcasing exactly that flaw.
"Can a machine trick a human" and "Can a machine think" are 2 very different questions. Humans can, and have shown to, be tricked by ELIZA and even simpler chatterbots, engines that don't even use any kind of ML, just large bodies of prewritten text and a number of static rules.
> by % of people who believe they are taking to a person.
And what does that denote? Say I get 2 groups of people. One is tricked by ELIZA 80% of the time, the other is tricked by ELIZA 40% of the time. Does that show that ELIZA passed or failed? Neither. It shows that the outcome of the test depends as much, or even more, on the ability of the interrogator than it does on the quality of the machines responses.
Imagine a litmus test (a chemical test to roughly determine the acidity of a solution), where the test result depends on who performs it, as much or even more than it does on the quality of the Litmus-Paper. No lab would use that test for obvious reasons.
Hayes and Ford : https://www.researchgate.net/publication/220813820_Turing_Te...
1995.
I'd rather call it "passing a milestone" or simply "progress". More specifically it just means most criteria, especially the Turing test, are poorly defined.
You're literally moving the "moving the goalposts" goalpost to make it more palatable.
> More specifically it just means most criteria, especially the Turing test, are poorly defined.
Nonsense. The Turing test was widely accepted for the specific reason that it contained a concrete method of testing for Artificial Intelligence. Go look for a better-defined goalpost for intelligence. You'll find pile after steaming pile of meaningless philosophical hair-splitting, each so completely divorced from reality as to make it useless as a real-world measuring stick.
Turing's test was and is the best measure of 'intelligence'. LLMs have passed the test. They are intelligent. Narrowly so, and utterly without agency, but they're clearly intelligent. There's no need to move the goalposts so that we can feel better about our place in the universe.
It was never supposed to be an end-all-be-all, it was supposed to be a quick and dirty way of eliminating possibilities.
There's an entire population out there convinced it's a human-level intelligence. If something deserves a passing grade, it's GPT.
- The interrogator is against two players, a bot and a human, and can ask questions to both of them, both try to convince the interrogator that they are the human, and in the end, the interrogator must tell which one is which.
- The test is to be done by experts, both the human player and the interrogator are expected to play to win. The goal is not to have some chit-chat and try to guess afterwards, the goal is to actively try to find the bot, and come in well prepared.
For now, there is absolutely no way GPT-4 can pass a real Turing test. Remember, both the human player and the interrogator are trained in bot detection, and they collaborate. The only thing that is not allowed is for the two humans to know each other from before as to not use shared secrets against the bot. But using common knowledge anti-bot techniques together is fair game.
The thing described in the paper is a weak variant of the Turing test that only tests the ability of AI designers to trick unsuspecting humans.
People are getting burned by interactions with bad bots and actors on the internet. Whereas in the past people had no experience with these digital personas, they likely put far more assumption in that a persona was human. As time goes on it seems likely that we'll assume that the actor is digital. Especially in cases where we're dealing with people from other cultures that don't share the same closely held values, we'll assume they are not real.