12 citations · 19 across the 4 of their papers we have counts for
5 papers · 1 filter
Pangram 4 Technical Report
Ben Glickenhaus, Katherine Thai, Jenna Russell +4
We present Pangram 4, the latest deep-learning-based AI-text classification model from Pangram Labs. We achieve an AUROC of 0.9916 with a false positive rate of 0.0041% and a false…
EditLens: Quantifying the Extent of AI Editing in Text
Katherine Thai, Bradley Emi, Elyas Masrour +1
A significant proportion of queries to large language models ask them to edit user-provided text, rather than generate new text from scratch. While previous work focuses on detecti…
AI use in American newspapers is widespread, uneven, and rarely disclosed
Jenna Russell, Marzena Karpinska, Destiny Akinode +4
AI is rapidly transforming journalism, but the extent of its use in published newspaper articles remains unclear. We address this gap by auditing a large-scale dataset of 186K arti…
DAMAGE: Detecting Adversarially Modified AI Generated Text
Elyas Masrour, Bradley Emi, Max Spero
AI humanizers are a new class of online software tools meant to paraphrase and rewrite AI-generated text in a way that allows them to evade AI detection software. We study 19 AI hu…
Technical Report on the Pangram AI-Generated Text Classifier
Bradley Emi, Max Spero
We present Pangram Text, a transformer-based neural network trained to distinguish text written by large language models from text written by humans. Pangram Text outperforms zero-…