LLMs Corrupt Your Documents When You Delegate: Our large-scale experiment with 19 LLMs reveals that […] even frontier models corrupt an average of 25% of document content by the end of long workflows
https://arxiv.org/abs/2604.15597
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This is where commit based version control can help mitigate the damage or detect it early. The golden rule in AI assistance in programming (if you’re going to use it) is to check the changes to make sure you understand everything before you commit it. If documents had a git-like version control system it would be easier to detect corruption early.
Or just use Markdown files in a git repo IDK
That’s not how humans work. We’re lazy.
You ever use snapshot tests? They’re garbage. They’re garbage because people glance at a 300 line diff and go “that seems right”, because a good chunk of the time it is. But some portion of the time, it’s borked up.
Having it in git doesn’t solve that problem.
That’s not how humans work. We’re lazy.
I’m not. Stop being lazy. Review your damn code.
LLMs don’t work if you don’t put the effort in. They save time when you do.
Or just use Markdown files in a git repo IDK
my pleas to my new management to do this fall on deaf ears, but they’re gung-ho about ai.
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Yes, this has been known for 10 years.
huh? the kind of “long workflows” this paper is discussing didn’t exist two years ago much less 10
it doesn’t matter. the principle is that if x is the length of your context window, then at 0.4x the chance of hallucinations start increasing exponentially. we’re now at token windows of 1M, and all it does is shift that hallucination window further away, so the model ‘feels’ stronger because it takes longer before it hallucinates, but eventually it always does.