Have that eerie feeling of déjà vu when reading model-generated text 👀, but can’t pinpoint the specific words or phrases 👀?
✨We introduce QUDsim, to quantify discourse similarities beyond lexical, syntactic, and content overlap.
Check out our paper for more results and analysis!
📝 arxiv.org/abs/2504.09373
🐙 github.com/AlliteraryAl...
This was a fun collaboration with @yatingwu.bsky.social @asher-zheng.bsky.social @manyawadhwa.bsky.social @gregdnlp.bsky.social @jessyjli.bsky.social
The “LLM vibe” is real even when the actual content is different. Across several genres from creative writing to obituaries, different LLMs generate homogenous discourse compared to humans.
QUDsim assigns a similarity score between two documents. It works by considering to what extent one document answers another's QUDs, and vice versa. Segment alignments between the texts can also be derived.
As large language models become increasingly capable at various writing tasks, their weakness at generating unique and creative content becomes a major liability. Although LLMs have the ability to gen...
