3 citations · 11 across the 11 of their papers we have counts for
13 papers
How Much Is an AI Token Worth? Scaling Laws for Wild AI-Generated Web Text
Jenna Russell, Ben Glickenhaus, Katherine Thai +4
Web text makes up the majority of pretraining data and is increasingly AI-generated. After applying FineWeb quality filtering, we find that 27.5% of tokens from June 2026 web data…
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…
BEARCUBS: A benchmark for computer-using web agents
Yixiao Song, Katherine Thai, Chau Minh Pham +3
Modern web agents possess computer use abilities that allow them to interact with webpages by sending commands to a virtual keyboard and mouse. While such agents have considerable…
One Thousand and One Pairs: A "novel" challenge for long-context language models
Marzena Karpinska, Katherine Thai, Kyle Lo +2
Synthetic long-context LLM benchmarks (e.g., "needle-in-the-haystack") test only surface-level retrieval capabilities, but how well can long-context LLMs retrieve, synthesize, and…
Iteratively Prompting Multimodal LLMs to Reproduce Natural and AI-Generated Images
Ali Naseh, Katherine Thai, Mohit Iyyer +1
With the digital imagery landscape rapidly evolving, image stocks and AI-generated image marketplaces have become central to visual media. Traditional stock images now exist alongs…
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…
ChapterBreak: A Challenge Dataset for Long-Range Language Models
Simeng Sun, Katherine Thai, Mohit Iyyer
While numerous architectures for long-range language models (LRLMs) have recently been proposed, a meaningful evaluation of their discourse-level language understanding capabilitie…
RELIC: Retrieving Evidence for Literary Claims
Katherine Thai, Yapei Chang, Kalpesh Krishna +1
Humanities scholars commonly provide evidence for claims that they make about a work of literature (e.g., a novel) in the form of quotations from the work. We collect a large-scale…
Maximum Covering Subtrees for Phylogenetic Networks
Nathan Davidov, Amanda Hernandez, Justin Jian +9
Tree-based phylogenetic networks, which may be roughly defined as leaf-labeled networks built by adding arcs only between the original tree edges, have elegant properties for model…