Publications (6)
When Copilot Becomes Autopilot: Generative AI's Critical Risk to Knowledge Work and a Critical Solution
Advait Sarkar, Xiaotong, Xu +3
Generative AI, with its tendency to "hallucinate" incorrect results, may pose a risk to knowledge work by introducing errors. On the other hand, it may also provide unprecedented o…
"It makes you think": Provocations Help Restore Critical Thinking to AI-Assisted Knowledge Work
Ian Drosos, Advait Sarkar, Xiaotong +2
Recent research suggests that the use of Generative AI tools may result in diminished critical thinking during knowledge work. We study the effect on knowledge work of provocations…
Somewhere Around That Number: An Interview Study of How Spreadsheet Users Manage Uncertainty
Judith Borghouts, Andrew D. Gordon, Advait Sarkar +2
Spreadsheet users regularly deal with uncertainty in their data, for example due to errors and estimates. While an insight into data uncertainty can help in making better informed…
Solving Data-centric Tasks using Large Language Models
Shraddha Barke, Christian Poelitz, Carina Suzana Negreanu +10
Large language models (LLMs) are rapidly replacing help forums like StackOverflow, and are especially helpful for non-professional programmers and end users. These users are often…
Running Probabilistic Programs Backwards
Neil Toronto, Jay McCarthy, David Van Horn
Many probabilistic programming languages allow programs to be run under constraints in order to carry out Bayesian inference. Running programs under constraints could enable other…
"What It Wants Me To Say": Bridging the Abstraction Gap Between End-User Programmers and Code-Generating Large Language Models
Michael Xieyang Liu, Advait Sarkar, Carina Negreanu +4
Code-generating large language models translate natural language into code. However, only a small portion of the infinite space of naturalistic utterances is effective at guiding c…