Tootfinder

🔊 #NowPlaying on BBCRadio3's #LateJunction
Martin Denny:
🎵 Quiet Village
#MartinDenny
https://open.spotify.com/track/3ZI49HJE66dhI3EvYHDqSz

Perusing the #AARP benefits, as one does. Most likely Bonefish Grill, Joe's Crab Shack, and Denny's. Maybe an 8-month Goldman-Sach's CD. Hot date, anyone?

Former GOP representative mulling comeback bid in Montana (Ally Mutnick/Politico)
https://www.politico.com/news/2024/02/07/danny-rehberg-montana-election-00140171

Large Language Models Meet User Interfaces: The Case of Provisioning Feedback
Stanislav Pozdniakov, Jonathan Brazil, Solmaz Abdi, Aneesha Bakharia, Shazia Sadiq, Dragan Gasevic, Paul Denny, Hassan Khosravi
https://arxiv.org/abs/2404.11072

Large Language Models Meet User Interfaces: The Case of Provisioning Feedback
Incorporating Generative AI (GenAI) and Large Language Models (LLMs) in education can enhance teaching efficiency and enrich student learning. Current LLM usage involves conversational user interfaces (CUIs) for tasks like generating materials or providing feedback. However, this presents challenges including the need for educator expertise in AI and CUIs, ethical concerns with high-stakes decisions, and privacy risks. CUIs also struggle with complex tasks. To address these, we propose transiti…

Automating Personalized Parsons Problems with Customized Contexts and Concepts
Andre del Carpio Gutierrez, Paul Denny, Andrew Luxton-Reilly
https://arxiv.org/abs/2404.10990

Automating Personalized Parsons Problems with Customized Contexts and Concepts
Parsons problems provide useful scaffolding for introductory programming students learning to write code. However, generating large numbers of high-quality Parsons problems that appeal to the diverse range of interests in a typical introductory course is a significant challenge for educators. Large language models (LLMs) may offer a solution, by allowing students to produce on-demand Parsons problems for topics covering the breadth of the introductory programming curriculum, and targeting thema…

Premise Order Matters in Reasoning with Large Language Models
Xinyun Chen, Ryan A. Chi, Xuezhi Wang, Denny Zhou
https://arxiv.org/abs/2402.08939 https://…

Transformers Can Achieve Length Generalization But Not Robustly
Yongchao Zhou, Uri Alon, Xinyun Chen, Xuezhi Wang, Rishabh Agarwal, Denny Zhou
https://arxiv.org/abs/2402.09371

Nonlinear spiked covariance matrices and signal propagation in deep neural networks
Zhichao Wang, Denny Wu, Zhou Fan
https://arxiv.org/abs/2402.10127 https…

Premise Order Matters in Reasoning with Large Language Models
Xinyun Chen, Ryan A. Chi, Xuezhi Wang, Denny Zhou
https://arxiv.org/abs/2402.08939 https://…

"Like a Nesting Doll": Analyzing Recursion Analogies Generated by CS Students using Large Language Models
Seth Bernstein, Paul Denny, Juho Leinonen, Lauren Kan, Arto Hellas, Matt Littlefield Sami Sarsa, Stephen MacNeil
https://arxiv.org/abs/2403.09409

"Like a Nesting Doll": Analyzing Recursion Analogies Generated by CS Students using Large Language Models
Grasping complex computing concepts often poses a challenge for students who struggle to anchor these new ideas to familiar experiences and understandings. To help with this, a good analogy can bridge the gap between unfamiliar concepts and familiar ones, providing an engaging way to aid understanding. However, creating effective educational analogies is difficult even for experienced instructors. We investigate to what extent large language models (LLMs), specifically ChatGPT, can provide acce…