ramyanamuduri.bsky.social
Ramya Namuduri
@ramyanamuduri.bsky.social
41 followers 23 following 4 posts
Undergrad at UT Austin in CS and Linguistics
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ramyanamuduri.bsky.social
Ramya Namuduri @ramyanamuduri.bsky.social · Apr 21
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
QUDsim: Quantifying Discourse Similarities in LLM-Generated Text
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...
arxiv.org
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ramyanamuduri.bsky.social
Ramya Namuduri @ramyanamuduri.bsky.social · Apr 21
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.
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ramyanamuduri.bsky.social
Ramya Namuduri @ramyanamuduri.bsky.social · Apr 21
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.
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ramyanamuduri.bsky.social
Ramya Namuduri @ramyanamuduri.bsky.social · Apr 21
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.
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