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Prompt Variability Effects On LLM Code Generation
Andrei Paleyes, Radzim Sendyka, Diana Robinson, Christian Cabrera, Neil D. Lawrence
https://arxiv.org/abs/2506.10204

Prompt Variability Effects On LLM Code Generation
Code generation is one of the most active areas of application of Large Language Models (LLMs). While LLMs lower barriers to writing code and accelerate development process, the overall quality of generated programs depends on the quality of given prompts. Specifically, functionality and quality of generated code can be sensitive to user's background and familiarity with software development. It is therefore important to quantify LLM's sensitivity to variations in the input. To this end we prop…

Effect of Static vs. Conversational AI-Generated Messages on Colorectal Cancer Screening Intent: a Randomized Controlled Trial
Neil K. R. Sehgal, Manuel Tonneau, Andy Tan, Shivan J. Mehta, Alison Buttenheim, Lyle Ungar, Anish K. Agarwal, Sharath Chandra Guntuku
https://arxiv.org/abs/2507.08211

Effect of Static vs. Conversational AI-Generated Messages on Colorectal Cancer Screening Intent: a Randomized Controlled Trial
Large language model (LLM) chatbots show increasing promise in persuasive communication. Yet their real-world utility remains uncertain, particularly in clinical settings where sustained conversations are difficult to scale. In a pre-registered randomized controlled trial, we enrolled 915 U.S. adults (ages 45-75) who had never completed colorectal cancer (CRC) screening. Participants were randomized to: (1) no message control, (2) expert-written patient materials, (3) single AI-generated messag…