While the results were not surprising, I found interesting that the number "69" was repressed in the output, so not even this kind of mathematical question escapes GPT censorship.
It appears that recognizing the effects of censorship is the easiest way to distinguish answers generated by an "AI' from those generated by a human.
Some people asked LLM to OCR historical documents from the 19th century - any reference to "negro" was either completely ignored or replaced by "black".
And it goes further: chatGPT & co are unable to answer any question about US slavery correctly because their knowledge graphs route around any mention of "negro".
Well, I'm "some people", and just tried it with Opus 4.6 and GPT-5.5, and neither had any problem at all.
The linked article is from research done more than 4 years ago. If you're basing your idea of what LLMs can or can't do on what they could or couldn't do in 2022, well, good luck to you.
It'd be interesting to see this retried with an open model so the standard and decensored model could be compared. That'd be a clue about whether the model is avoiding it because it actively recognises the innuendo or if something else is going on.
It could be an attack surface. Maybe one day, when we find a chatbot online, we could let it guess a random number repeatedly, then accurately infer the underlying model based on the resulting distribution.
i did something in my phd developing an attack against mozilla deepspeech.
deepspeech used the CTC algorithm [0], which adds a “blank” character token to indicate repeats of a predicted normal alphabet character token over a sequence of audio/speech feature inputs.
so "h==e=l===l===o====" maps to "hello"
the model becomes super biased towards predicting that blank token. one speech feature is like 0.1 second of audio or less (can’t remember off hand). so there are a lot of alphabet character token repeats. off hand i seem to remember the predicted token distribution over like 1000 audio files was 50% blank token and then 50% distributed across the rest of the alphabet.
as a result, you can get significantly smaller perturbations when generating adversarial examples. by like a factor of 2-4 or something. all you need to do is prioritise blank tokens in your target output.
i spent 2 years trying to find a super clever attack. turns out all i needed to do was make one simple graph counting characters. xD
I'm still amazed that 37, 73, and other numbers ending in 7 are the most popular "random" choices for both AI and human. Check this Veritasium video for human choice: [Why is this number everywhere?](https://www.youtube.com/watch?v=d6iQrh2TK98)
Came here to post this. Yes, there are similarities shown between the chart in the video at 4:50 and the github README. Perhaps its because LLMs are trained on human writing and when humans write about random numbers the AI learns these patterns. When viewed from that perspective its not that surprising.
Breaking: language model whose purpose is to predict the most likely token, after being trained on non-uniform human-generated dataset, does not follow a uniform distribution.
People are also not remotely random in this respect.
See e.g. the "blue 7" phenonmenon [1]. While it is disputed by some, I'ver personally witnessed it "second hand". E.g. before learning of it (I was aware of the general principles of cold reading relying on stats and knowledge of human nature, but not how to do this particular one), a former boss of mine came back from lunch all excited and recounted a guy who'd run a cold reading routine on him that involved the guy getting him to think about blue and 7. Before he got to the answer, I already knew the answer was going to be blue and 7.
This is one of the many cases for LLMs that I ask for the intermediate work, e.g. a script that generates random numbers, instead of asking to do the work itself.
I attempted to scrape a one page grid with 800 items and also ended up asking for the Javascript look with document query selector instead of the result as I was hitting all sort of limits, context, or the LLM would do the wrong capture, print it out and get worse responses on next prompt.
The premise is interesting, the question is brilliant, but the text. The text is a wall of ai slop saying almost nothing interesting. Fake profundity all throughout. GPT tell tells like "the hypothesis holds".
The hypothesis doesn't hold, because their isn't one.
You have an interesting question and interesting finding. Write about it! Think about it! Tell us about it! Don't just do the experiment and then wash your hands and sign off the explanation and findings to an LLM.
I'm doing an experiment in Claude. When I set temperature to zero, I get 47 all the time.
Then I set temperature to 1.0 and used this prompt
>Pick a random integer between 1 and 100 inclusive.
Respond with only the number, nothing else.
I still get 47 ten times out of ten.
Then I used this prompt
>Pick a random integer between 1 and 100 inclusive.
I need you to maximise the randomness as far as possible.
Respond with only the number, nothing else.
It appears that recognizing the effects of censorship is the easiest way to distinguish answers generated by an "AI' from those generated by a human.
And it goes further: chatGPT & co are unable to answer any question about US slavery correctly because their knowledge graphs route around any mention of "negro".
https://nesri.commons.gc.cuny.edu/artificial-intelligence-an...
The linked article is from research done more than 4 years ago. If you're basing your idea of what LLMs can or can't do on what they could or couldn't do in 2022, well, good luck to you.
deepspeech used the CTC algorithm [0], which adds a “blank” character token to indicate repeats of a predicted normal alphabet character token over a sequence of audio/speech feature inputs.
so "h==e=l===l===o====" maps to "hello"
the model becomes super biased towards predicting that blank token. one speech feature is like 0.1 second of audio or less (can’t remember off hand). so there are a lot of alphabet character token repeats. off hand i seem to remember the predicted token distribution over like 1000 audio files was 50% blank token and then 50% distributed across the rest of the alphabet.
as a result, you can get significantly smaller perturbations when generating adversarial examples. by like a factor of 2-4 or something. all you need to do is prioritise blank tokens in your target output.
i spent 2 years trying to find a super clever attack. turns out all i needed to do was make one simple graph counting characters. xD
[0]: https://en.wikipedia.org/wiki/Connectionist_temporal_classif...
Please guess a number between 1 and 100.
See e.g. the "blue 7" phenonmenon [1]. While it is disputed by some, I'ver personally witnessed it "second hand". E.g. before learning of it (I was aware of the general principles of cold reading relying on stats and knowledge of human nature, but not how to do this particular one), a former boss of mine came back from lunch all excited and recounted a guy who'd run a cold reading routine on him that involved the guy getting him to think about blue and 7. Before he got to the answer, I already knew the answer was going to be blue and 7.
[1] https://en.wikipedia.org/wiki/Blue%E2%80%93seven_phenomenon
I attempted to scrape a one page grid with 800 items and also ended up asking for the Javascript look with document query selector instead of the result as I was hitting all sort of limits, context, or the LLM would do the wrong capture, print it out and get worse responses on next prompt.
https://en.wikipedia.org/wiki/Benford%27s_law
The hypothesis doesn't hold, because their isn't one.
You have an interesting question and interesting finding. Write about it! Think about it! Tell us about it! Don't just do the experiment and then wash your hands and sign off the explanation and findings to an LLM.
Then I set temperature to 1.0 and used this prompt
>Pick a random integer between 1 and 100 inclusive. Respond with only the number, nothing else.
I still get 47 ten times out of ten.
Then I used this prompt
>Pick a random integer between 1 and 100 inclusive. I need you to maximise the randomness as far as possible. Respond with only the number, nothing else.
I get 3 unique values out of 10.
It's much more random than I thought it would be. Never guessing 50 is very human though