7 citations · 20 across the 20 of their papers we have counts for
30 papers · 1 filter
Generative AI floods and dilutes the market for books
Tuhin Chakrabarty, Xinyue Liu, Jane C. Ginsburg +1
Generative AI can produce book-length works of fiction at near-zero cost. These books are often dismissed as low-quality ``slop'' that buyers will ignore, and are assumed to carry…
Alignment Whack-a-Mole : Finetuning Activates Verbatim Recall of Copyrighted Books in Large Language Models
Xinyue Liu, Niloofar Mireshghallah, Jane C. Ginsburg +1
Frontier LLM companies have repeatedly assured courts and regulators that their models do not store copies of training data. They further rely on safety alignment strategies via RL…
Readers Prefer Outputs of AI Trained on Copyrighted Books over Expert Human Writers
Tuhin Chakrabarty, Jane C. Ginsburg, Paramveer Dhillon
The use of copyrighted books for training AI has sparked lawsuits from authors concerned about AI generating derivative content. Yet whether these models can produce high-quality l…
Death of the Novel(ty): Beyond n-Gram Novelty as a Metric for Textual Creativity
Arkadiy Saakyan, Najoung Kim, Smaranda Muresan +1
N-gram novelty is widely used to evaluate language models' ability to generate text outside of their training data. More recently, it has also been adopted as a metric for measurin…
Measuring AI "Slop" in Text
Chantal Shaib, Tuhin Chakrabarty, Diego Garcia-Olano +1
AI "slop" is an increasingly popular term used to describe low-quality AI-generated text, but there is currently no agreed upon definition of this term nor a means to measure its o…
AI-Slop to AI-Polish? Aligning Language Models through Edit-Based Writing Rewards and Test-time Computation
Tuhin Chakrabarty, Philippe Laban, Chien-Sheng Wu
AI-generated text is proliferating across domains, from creative writing and journalism to marketing content and scientific articles. Models can follow user-provided instructions t…