Ramya Namuduri
@ramyanamuduri.bsky.social
Undergrad at UT Austin in CS and Linguistics
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
📝 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
April 21, 2025 at 9:31 PM
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
📝 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.
April 21, 2025 at 9:30 PM
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.
April 21, 2025 at 9:30 PM
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.