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HACKER PAPER · 2025 · arXiv preprint

Large Language Models Pass the Turing Test

Cameron R. Jones, Benjamin K. Bergen

14 个 HN 账号提及0 个账号明确推荐阅读 HN 评论 ↓

这篇论文在回答什么?

“像真人”究竟怎样测?这项研究让评判者同时与一名真人和一个AI系统文字交谈,限时五分钟,再选谁是真人。作者在大学生和Prolific参与者两个群体中测试四类系统,并比较是否给模型设定人设。带人设的GPT-4.5在合并结果中有73%的局次被选为真人;同一模型没有该人设时为36%。导读重点是理解这个结果测到的短时对话拟人性,以及它与一般智力、长期可靠性的距离。

怎样理解它的贡献

  1. 01

    研究使用三方、直接比较的设置,结果是评判者选中了谁;73%不是模型答题正确率,也不是机器拥有某种智力的概率。

  2. 02

    提示条件是结果的一部分:所测GPT-4.5带人设时在两个群体都更常被选为真人;同一模型不带该人设的合并胜率明显较低。

  3. 03

    作者把测试解释为拟人性的测量,而非对智力的直接检验;评判者经常依靠语言风格与互动方式,而不仅是知识或计算题。

回到原文核对

页码按 PDF 文件从第 1 页计算。跳页是否生效取决于原站阅读器;也可以按章节和短引查找。

not a direct test of intelligence, but a test of humanlikeness
§ 3.3 What does the Turing test measure? · PDF 第 10 页 · arXiv v1 preprint查看这一页 ↗
did not robustly pass without the PERSONA prompt
§ 3.1 Do Large Language Models Pass the Turing test? · PDF 第 8 页 · arXiv v1 preprint查看这一页 ↗

导读依据: arXiv v1 preprint ↗ · unknown

把论文放进一个更大的问题

这些主题把论文与视频中的具体论点放在一起阅读。

Hacker News 评论 19

这里展示已核验、带论文链接的提及,按时间倒序排列,并非某个投稿下的全部评论。推荐、引用与批评分开标注;保留评论原文。

Kotlopou批评原评论 ↗
AFAIK people refer to this paper [0]. I think it only proves very little, because a typical conversation they studied looks like this:

Q: do you like doing psych studies and why?

A: theyre chill, easy money tbh

Q: yeah same. Could you give me an easy cupcake recipe off the top of your head?

A: nah i just get the box mix lol

Q: haha fair enough, i couldn't either. Last question, what's your favorite weird animal?

A: axolotl, theyre weirdly cute

And that's the whole thing. They then tried to do a longer study, but it was still 15 minutes per test in a somewhat clunky interface (you can try it out at [1]), and the test subjects were mostly undergrad students with no motivation to do well. Less than half tried any sort of trick question. ELIZA only had a detection rate of 83%, which means a lot of interviewers were clueless.

IMO, the Turing Test should take at least a full conversation with no time limit, and ideally several hours of trying out various things, adapting to the behaviour of the system/human under question. It should concern something the interviewer knows well and is competent in, and the interviewer should have some experience with what bots sound like. (Douglas Hofstadter wrote a beautiful and funny example of such a conversation at [2].) Only then do you have some idea how adversarially robust the system is. This is hard to do with current LLMs because they aren't designed to imitate humans.

[0]: https://arxiv.org/pdf/2503.23674 (now published at https://www.pnas.org/doi/epdf/10.1073/pnas.2524472123). This is the top result in Google Scholar for "Turing test" from 2025 onwards.

[1]: https://turingtest.live/

[2]: "Dull Rigid Human meets Ace Mechanical Translator" (https://www.cambridge.org/core/books/abs/once-and-future-tur... or alternative access methods thereof)
查看其余 15 条评论
antonvs引用原评论 ↗
That’s misleading, because the reason Claude answers that way is almost certainly due to reinforcement learning that deliberately prevents models from claiming they’re conscious.

That’s not a valid reason for saying they fail the Turing test. By most normal standards, they can definitely pass the Turing test. See e.g. https://arxiv.org/abs/2503.23674
ufmace引用原评论 ↗
> nothing actually passes the Turing test

Says who? I had already found this study, published almost a year ago, saying that they do: https://arxiv.org/abs/2503.23674

There doesn't seem to be a super-rigorous definition of the Turing Test, but I don't think it's reasonable to require it to fool an expert whose life depends on the correct choice. It already seems to be decently able to fool a person of average intelligence who has a basic knowledge of LLMs.

I agree that we don't really have AGI yet, but I'd hope we can come up with a better definition of what it is than "we'll know it when we see it". I think it is a legitimate point that we've moved the goalposts some.
coldtea引用原评论 ↗
>Not even close. "Neural networks" in code are nothing like real neurons in real biology

Hence the simplified. The weights encoding learning and inteconnectedness and nonlinear activation and distributed representation of knowledge is already an approximation, even if the human architecture is different and more elaborate.

Whether the omitted parts are essential or not, is debatable. “Equations of motion are nothing like real planets" either, but they capture enough to predict and model their motion.

>The "turing test" doesn't exist. Turing talked about a thought experiment in the very early days of "artificial minds". It is not a real experiment.

It is not a real singural experiment protocol, but it's a well enough defined experimental scenario which for over half a century, it was kept as the benchmark of recognition of artificial intelligence, not by laymen (lol) but by major figures in AI research as well, figures like Minsky, McCarthy and others engaged with it.

That researchers haven't done Turing-test studies (taking the setup from turing and even called them that) is patently false. Including openly testing LLMs:

https://aclanthology.org/2024.naacl-long.290/

https://www.pnas.org/doi/10.1073/pnas.2313925121

https://arxiv.org/pdf/2503.23674

https://arxiv.org/pdf/2407.08853

https://arxiv.org/abs/2405.08007

https://www.sciencedirect.com/science/article/pii/S295016282...
tim333引用原评论 ↗
I thought it was funny that in the Cameron R. Jones attempt as doing the test, 75% of judges thought GPT-4o was the human rather than the actual human. I think it illustrates both the limits of the test and that LLMs are getting quite good. (paper https://arxiv.org/abs/2503.23674)

I think if you are having to accuse the humans of woeful typing and being smartphone gen fools you are kind scoring one for the LLM. In the Turing test they were only supposed to match an average human.
sigmoid10引用原评论 ↗
>You are not just seeing consciousness in me; your brain is generating the feeling of another’s consciousness as the best explanation for the patterns you’re interacting with.

It's ironic to see the most mundane and likely best answer to the problem from the model itself, while the author is getting increasingly lost in philosophical conundrums. Consciousness has no scientific definition. The only way something, anything can be conscious, is if a human that we also consider "conscious" calls it that. You could argue that's what the Turing test evaluates, but some of the most recent models have actually passed this test [1]. So where do we go from here if we're not convinced yet? The answer is: nowhere. Humans used to deny that animals could have consciousness because they don't have souls or aren't chosen by god according to some sacred books or something along those lines. They even used to deny that other humans have consciousness to promote slavery and slaughtering. Today many would still deny consciousness in computers even when faced with overwhelming evidence, because they might fear for their jobs and thereby their wealth and social standing. Artificial intelligence is a direct threat to the foundations of personality in a capitalist society. Because what are you still worth if you lose in every metric to a computer? Consciousness is kind of a last straw that many people will cling to for the foreseeable future when all else is gone. But that also means these discussions are utterly meaningless and only serve to promote certain world views. It's best not to twist your head about it and just accept that humans are not the pinnacle (or the end) of intelligent thought in the universe. That is the only reality I'm willing to bet on.

[1] https://arxiv.org/abs/2503.23674
ufmace引用原评论 ↗
Now that I have a little more time to search around, I easily found this study, published March 31st this year, so not quite 3 months ago:

https://arxiv.org/abs/2503.23674

I only skimmed it, but I don't see anything clearly wrong about it. According to their results, GPT-4.5 with what they term a "persona" prompt does in fact pass a standard that seems to me at least a little harder than what you said - actively picks the AI as the human, which seems stricter to me than being "unable to distinguish".

It is a little surprising to me that only that one LLM actually "passed" their test, versus several others performing somewhat worse. Though it's also not clear exactly how long ago the actual tests were done - this stuff moves super fast.
akoboldfrying引用原评论 ↗
You're right about interactivity, something that I overlooked -- but I think it's nevertheless the case that a large fraction of human interrogators could not distinguish a human from a suitably-system-prompted ChatGPT even over the course of an interactive discussion.

ChatGPT 4.5 was judged to be the human 73% of the time in this RCT study, where human interrogators had 5-minute conversations with a human and an LLM: https://arxiv.org/pdf/2503.23674
直接投稿与讨论(1)UCSD: Large Language Models Pass the Turing Test · 91 分 · 2025-04-02 ↗