This post was rejected for the following reason(s):
No LLM generated, heavily assisted/co-written, or otherwise reliant work. Hey man, our policy on LLM-written content isn't a suggestion.
This post was rejected for the following reason(s):
No LLM generated, heavily assisted/co-written, or otherwise reliant work. Hey man, our policy on LLM-written content isn't a suggestion.
As someone newly engaging with LessWrong, I recently shared a post exploring internal modulation mechanisms in Transformers, inspired by affective dynamics in human cognition. It was my first contribution, and it was not approved—primarily, I was told, because it resembled AI-generated content in style.
I understand that LessWrong upholds high standards for originality and depth, especially in an era where language models can produce fluent but shallow writing. I appreciate that intention, and I agree with the spirit behind the policy. Still, I’d like to offer a constructive reflection—one that I hope contributes to refining our shared norms.
From what I gather, the current AI-writing policy discourages submissions that either rely too heavily on model outputs or resemble a "stereotypical AI assistant style," even if the ideas are original and human-developed. This creates a tension: authors who express themselves with polished prose or technical fluency—especially newcomers—may inadvertently be flagged for style alone.
My concern is that focusing too much on surface-level indicators can risk filtering out thoughtful contributions before their substance is even evaluated. The issue is not with rejecting low-effort AI copy-pastes—that seems reasonable—but with the possibility that valuable work might be dismissed due to its resemblance to AI-written text rather than based on its merit.
One possible refinement could be to continue requiring transparent disclosure of AI involvement (if any), while prioritizing evaluations based on content quality: clarity, coherence, novelty, and intellectual contribution. Encouraging authors to document how they used tools—if at all—and what they added could preserve quality while reducing the risk of false positives.
Ultimately, I see this as a calibration challenge, not a fundamental flaw. I’m grateful to engage with a community that cares deeply about epistemic integrity, and I hope we can keep evolving our standards in ways that remain inclusive of new voices—especially those earnestly trying to contribute to long-range alignment and rationalist thought.