3 citations · 9 across the 9 of their papers we have counts for
8 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…
Literary Evidence Retrieval via Long-Context Language Models
Katherine Thai, Mohit Iyyer
How well do modern long-context language models understand literary fiction? We explore this question via the task of literary evidence retrieval, repurposing the RELiC dataset of…
Exploring Document-Level Literary Machine Translation with Parallel Paragraphs from World Literature
Katherine Thai, Marzena Karpinska, Kalpesh Krishna +4
Literary translation is a culturally significant task, but it is bottlenecked by the small number of qualified literary translators relative to the many untranslated works publishe…
DEMETR: Diagnosing Evaluation Metrics for Translation
Marzena Karpinska, Nishant Raj, Katherine Thai +3
While machine translation evaluation metrics based on string overlap (e.g., BLEU) have their limitations, their computations are transparent: the BLEU score assigned to a particula…