Towards Process-Oriented, Modular, and Versatile Question Generation that Meets Educational Needs. - Xu Wang 016; Simin Fan; Jessica Houghton; Lu Wang 008 (NAACL-HLT, 2022)

ReadingQuizMaker: A Human-NLP Collaborative System that Supports Instructors to Design High-Quality Reading Quiz Questions. - Xinyi Lu; Simin Fan; Jessica Houghton; Lu Wang 008; Xu Wang 016 (CHI, 2023)

Irreducible Curriculum for Language Model Pretraining. - Simin Fan; Martin Jaggi (CoRR, 2023)

MEDITRON-70B: Scaling Medical Pretraining for Large Language Models. - Zeming Chen 001; Alejandro Hernández-Cano; Angelika Romanou; Antoine Bonnet; Kyle Matoba; Francesco Salvi; Matteo Pagliardini; Simin Fan; Andreas Köpf; Amirkeivan Mohtashami; Alexandre Sallinen; Alireza Sakhaeirad; et al. (CoRR, 2023)

DOGE: Domain Reweighting with Generalization Estimation. - Simin Fan; Matteo Pagliardini; Martin Jaggi (ICML, 2024)

Deep Grokking: Would Deep Neural Networks Generalize Better? - Simin Fan; Razvan Pascanu; Martin Jaggi (CoRR, 2024)

Could ChatGPT get an Engineering Degree? Evaluating Higher Education Vulnerability to AI Assistants. - Beatriz Borges; Negar Foroutan; Deniz Bayazit; Anna Sotnikova; Syrielle Montariol; Tanya Nazaretzky; Mohammadreza Banaei; Alireza Sakhaeirad; Philippe Servant; Seyed Parsa Neshaei; Jibril Frej; Angelika Romanou; et al. (CoRR, 2024)

Dynamic Gradient Alignment for Online Data Mixing. - Simin Fan; David Grangier; Pierre Ablin (CoRR, 2024)

HyperINF: Unleashing the HyperPower of the Schulz's Method for Data Influence Estimation. - Xinyu Zhou; Simin Fan; Martin Jaggi (CoRR, 2024)

Task-Adaptive Pretrained Language Models via Clustered-Importance Sampling. - David Grangier; Simin Fan; Skyler Seto; Pierre Ablin (ICLR, 2025)

NeuralGrok: Accelerate Grokking by Neural Gradient Transformation. - Xinyu Zhou; Simin Fan; Martin Jaggi; Jie Fu (CoRR, 2025)

GRAPE: Optimize Data Mixture for Group Robust Multi-target Adaptive Pretraining. - Simin Fan; Maria Ios Glarou; Martin Jaggi (CoRR, 2025)

Semantic uncertainty in advanced decoding methods for LLM generation. - Darius Foodeei; Simin Fan; Martin Jaggi (CoRR, 2025)