Anne Hathaway received the same AI-written thank you note from every candidate—and Meryl Streep said what every boss is thinking: “That’s just tragic.”
It’s probably not the same word for word, but with very similar structures. And LLMs tend to structure the text in very similar ways that don’t feel quite right.
It seems a more likely that the candidates were cutting and pasting a standard response, either way my point was to question the integrity of the article, which seems itself to be AI slop anyway.
You are right, but you are also wrong. If they’re given the same seed, they certainly will. They are 100% deterministic. But in reality, the seed is randomly generated, so yeah, it won’t be exactly the same every time.
Even if they had the same seed, they would all need the exact same prompt. The chances of multiple people all independently coming up with the exact same prompt is highly unlikely.
Please read the last sentence of my comment. I am not saying that the interpretation is wrong, I’m saying the statement that that’s not how LLMs work is wrong. That is how LLMs work. They are deterministic. The only reason they don’t do that in practice is because we purposefully seed them with random data to make them not do that.
You can give an LLM the same seed and it will spit out the same word-for-word response. That’s how they work. It’s just a bunch of math.
You’re assuming that because I missed out that detail I must be ignorant of it, that’s not very charitable, I could well have been ignorant of it but you could have made your otherwise useful clarification without telling me I was wrong.
You said “that’s not how they work”. But that is how they work. Same prompt = same output. Throw some random data in there to jumble things around and you get a little variance. That’s the seed, and we only need to do that because LLMs are inherently deterministic.
Same reason Minecraft has a random seed for world generation, and block cipher algorithms use an initialization vector and/or feedback loop. We don’t want the same thing every time.
I did say that you’re right, because the tooling we use around the LLM itself does exactly what you’re talking about. So, in practice, you’re right.
Again, you’re telling me what I already know, because you’re still assuming. I can make the point that same prompts don’t produce the same output without explaining about random seeds.
I honestly wasn’t trying to attack you. I think we should be careful when we talk about LLMs, because it’s important for people to know that it’s just a bunch of math in a computer program. A lot of people have a tendency to anthropomorphize it.
So your hyopthesis is that instead of a load of people cutting and pasting the same response (AI generated or otherwise,) they all cut and pasted the exact same prompt into exactly the same model with exactly the same context running on exactly the same hardware, and went to the trouble of also fixing the same seed?
Something is wrong here, LLMs won’t spit out the same word-for-word response for the same prompt that’s not how they work.
It’s probably not the same word for word, but with very similar structures. And LLMs tend to structure the text in very similar ways that don’t feel quite right.
The article said they were the “exact same”.
Some reporters tend to take hyperbole too literally.
It seems a more likely that the candidates were cutting and pasting a standard response, either way my point was to question the integrity of the article, which seems itself to be AI slop anyway.
Or, more likely, that the article was generated to target hot keywords and is completely made up.
You are right, but you are also wrong. If they’re given the same seed, they certainly will. They are 100% deterministic. But in reality, the seed is randomly generated, so yeah, it won’t be exactly the same every time.
Even if they had the same seed, they would all need the exact same prompt. The chances of multiple people all independently coming up with the exact same prompt is highly unlikely.
Please read the last sentence of my comment. I am not saying that the interpretation is wrong, I’m saying the statement that that’s not how LLMs work is wrong. That is how LLMs work. They are deterministic. The only reason they don’t do that in practice is because we purposefully seed them with random data to make them not do that.
Where was I wrong? I said nothing that contradicts the detail you added.
You can give an LLM the same seed and it will spit out the same word-for-word response. That’s how they work. It’s just a bunch of math.
You’re assuming that because I missed out that detail I must be ignorant of it, that’s not very charitable, I could well have been ignorant of it but you could have made your otherwise useful clarification without telling me I was wrong.
You said “that’s not how they work”. But that is how they work. Same prompt = same output. Throw some random data in there to jumble things around and you get a little variance. That’s the seed, and we only need to do that because LLMs are inherently deterministic.
Same reason Minecraft has a random seed for world generation, and block cipher algorithms use an initialization vector and/or feedback loop. We don’t want the same thing every time.
I did say that you’re right, because the tooling we use around the LLM itself does exactly what you’re talking about. So, in practice, you’re right.
Again, you’re telling me what I already know, because you’re still assuming. I can make the point that same prompts don’t produce the same output without explaining about random seeds.
I honestly wasn’t trying to attack you. I think we should be careful when we talk about LLMs, because it’s important for people to know that it’s just a bunch of math in a computer program. A lot of people have a tendency to anthropomorphize it.
So your hyopthesis is that instead of a load of people cutting and pasting the same response (AI generated or otherwise,) they all cut and pasted the exact same prompt into exactly the same model with exactly the same context running on exactly the same hardware, and went to the trouble of also fixing the same seed?
That certainly seems the simpler explanation.
How did you possibly get that from what I said? Are you purposefully being as uncharitable as possible?
No, I clearly was not talking about this situation. I was clarifying how your interpretation was correct, but you were factually incorrect.
Not my interpretation.
And what you were doing was “well, akshuallying”. Own it.
It’s called pedantry, and I have never failed to own it. At this point, I feel like you’re trying to be overly abrasive.
Was it not your interpretation that the messages seem to not be from LLMs, because they’re identical? Because that’s literally what you said.You really need to learn to read who you are replying to.
fuck mate, they said as much