When German journalist Martin Bernklautyped his name and location into Microsoft’s Copilot to see how his articles would be picked up by the chatbot, the answers horrified him. Copilot’s results asserted that Bernklau was an escapee from a psychiatric institution, a convicted child abuser, and a conman preying on widowers. For years, Bernklau had served as a courts reporter and the AI chatbot had falsely blamed him for the crimes whose trials he had covered.
The accusations against Bernklau weren’t true, of course, and are examples of generative AI’s “hallucinations.” These are inaccurate or nonsensical responses to a prompt provided by the user, and they’re alarmingly common. Anyone attempting to use AI should always proceed with great caution, because information from such systems needs validation and verification by humans before it can be trusted.
But why did Copilot hallucinate these terrible and false accusations?
Is this true? I know that’s how Marcov chains work, but I thought neural nets worked differently with larger tokens.
The only difference between a generic old fashioned word salad generator and GPT4 is the scale. You put multiple layers correcting for different factors on it and suddenly your Language Model turns into a Large Language Model.
So basically your large tokens are made up of smaller tokens, but its still just statistical approximation of the sample data with little to no emergent behavior or even memory of what its saying as it says it.
It also exponentially increases power requirements, as the world is figuring out.
I don’t disagree, I was just pointing out that “each word is generated independently of each other” isn’t strictly accurate for LLM’s.
It’s part of the reason they are so convincing to some people, they are able to hold threads semi-coherently throughout entire essay length paragraphs without obvious internal lapses of logic.
I think you’re seeing coherence where there is none.
Ask it to solve the riddle about the fox the chicken and the grains.
Even if it does solve the riddle without blurting out random nonsense, that’s just because the sample data solved the riddle billions of times before.
It’s just guessing words.
I think it getting tripped up on riddles that people often fail or it not getting factual things correct isn’t as important for “believability”, which is probably a word closer to what I meant than “coherence.”
No one was worried about misinformation coming from r/SubredditSimulator, for example, because Marcov chains have much much less believability. “Just guessing words” is a bit of a over-simplification for neural nets, which are a powerful technology even if the utility of turning it towards language is debatable.
And if LLM’s weren’t so believable we wouldn’t be having so many discussions about the misinformation or misuse they could cause. I don’t think we’re disagreeing I’m just trying to add more detail to your “each word is generated independently” quote, which is patently wrong and detracts from your overall point.
lmao yeh bro such a hard riddle totally
I concede. AI has a superintelligient brain and I’m just so jealous. You have permission to whip me into submission.
What on Earth is this in response to?? Did I say it was a hard riddle?
Point to any part of my comment that implied any of this.
I only gave more info on how LLMs work since what you were describing were Marcov chains. I wasn’t saying you were wrong with the thrust of your comment, just the details on how they work. If they were exactly as effective as Marcov chains we wouldn’t be having these discussions, that’s why they can be misused.
Feel free to discuss the actual words I’m using instead of this LLM word salad.
Well clearly the AI couldn’t solve the riddle because even humans find it soooooo haaaard, daddy.
Are you in high school? You’re making up things I never said and putting a sexual element on your responses for no reason.
Stay in school and learn how to have discussions before arguing about language and technology lol
Goodnight.