132 citations · 151 across the 6 of their papers we have counts for
7 papers
FAVoR: Measuring and Mitigating Author-Style Homogenization in Federated Personalized Generation
Lu Han, Jingyao Zhang, Katy Ilonka Gero +1
Large language models are increasingly used as personalized writing assistants, but adapting a model across many authors can compromise individual writing style by pulling author-s…
Creative Writers' Attitudes on Writing as Training Data for Large Language Models
Katy Ilonka Gero, Meera Desai, Carly Schnitzler +3
The use of creative writing as training data for large language models (LLMs) is highly contentious and many writers have expressed outrage at the use of their work without consent…
Data Authenticity, Consent, & Provenance for AI are all broken: what will it take to fix them?
Shayne Longpre, Robert Mahari, Naana Obeng-Marnu +5
New capabilities in foundation models are owed in large part to massive, widely-sourced, and under-documented training data collections. Existing practices in data collection have…
A Design Space for Intelligent and Interactive Writing Assistants
Mina Lee, Katy Ilonka Gero, John Joon Young Chung +33
In our era of rapid technological advancement, the research landscape for writing assistants has become increasingly fragmented across various research communities. We seek to addr…
Supporting Sensemaking of Large Language Model Outputs at Scale
Katy Ilonka Gero, Chelse Swoopes, Ziwei Gu +2
Large language models (LLMs) are capable of generating multiple responses to a single prompt, yet little effort has been expended to help end-users or system designers make use of…
Sparks: Inspiration for Science Writing using Language Models
Katy Ilonka Gero, Vivian Liu, Lydia B. Chilton
Large-scale language models are rapidly improving, performing well on a wide variety of tasks with little to no customization. In this work we investigate how language models can s…
Low-Level Linguistic Controls for Style Transfer and Content Preservation
Katy Gero, Chris Kedzie, Jonathan Reeve +1
Despite the success of style transfer in image processing, it has seen limited progress in natural language generation. Part of the problem is that content is not as easily decoupl…