Papers by Jenna James
AI use in American newspapers is widespread, uneven, and rarely disclosed (2026.acl-long)
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Jenna Russell, Marzena Karpinska, Destiny Akinode, James Zhou, Katherine Thai, Bradley Emi, Max Spero, Mohit Iyyer
| Challenge: | a large-scale dataset of 186K articles from 1.5K newspapers published in the summer of 2025 is audited. |
| Approach: | They audit 186K articles from 1.5K newspapers published in summer of 2025 . they use Pangram, a state-of-the-art AI detector, to detect whether articles are partially or fully AI-generated . |
| Outcome: | The findings highlight the need for greater transparency and updated editorial standards regarding the use of AI in journalism to maintain public trust. |
OLMoTrace: Tracing Language Model Outputs Back to Trillions of Training Tokens (2025.acl-demo)
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Jiacheng Liu, Taylor Blanton, Yanai Elazar, Sewon Min, Yen-Sung Chen, Arnavi Chheda-Kothary, Huy Tran, Byron Bischoff, Eric Marsh, Michael Schmitz, Cassidy Trier, Aaron Sarnat, Jenna James, Jon Borchardt, Bailey Kuehl, Evie Yu-Yen Cheng, Karen Farley, Taira Anderson, David Albright, Carissa Schoenick, Luca Soldaini, Dirk Groeneveld, Rock Yuren Pang, Pang Wei Koh, Noah A. Smith, Sophie Lebrecht, Yejin Choi, Hannaneh Hajishirzi, Ali Farhadi, Jesse Dodge
| Challenge: | tracing language models' outputs back to training data is a problem because they are trained on text corpora with trillions of tokens . existing methods for tracers have not been scaled to work within this multi-trillion-token setting . |
| Approach: | They propose a system that traces language models' outputs verbatim back to training data . OLMOTRACE retrieves documents from the model's training data that contain exact matches . |
| Outcome: | The proposed system can find verbatim matches between LM output and training data . it can be used to explore fact checking, hallucination, and creativity of language models . |