29 citations · 48 across the 24 of their papers we have counts for
6 papers · 1 filter
Variational Model Merging for Pareto Front Estimation in Multitask Finetuning
Hugo Monzón Maldonado, Nico Daheim, Thomas Möllenhoff +2
Pareto fronts are useful to find good task-mixing strategies for multitask finetuning, but they are also costly to compute. To reduce costs, recent works have used existing model m…
Variational Low-Rank Adaptation Using IVON
Bai Cong, Nico Daheim, Yuesong Shen +4
We show that variational learning can significantly improve the accuracy and calibration of Low-Rank Adaptation (LoRA) without a substantial increase in the cost. We replace AdamW…
Stepwise Verification and Remediation of Student Reasoning Errors with Large Language Model Tutors
Nico Daheim, Jakub Macina, Manu Kapur +2
Large language models (LLMs) present an opportunity to scale high-quality personalized education to all. A promising approach towards this means is to build dialog tutoring models…
Book2Dial: Generating Teacher-Student Interactions from Textbooks for Cost-Effective Development of Educational Chatbots
Junling Wang, Jakub Macina, Nico Daheim +2
Educational chatbots are a promising tool for assisting student learning. However, the development of effective chatbots in education has been challenging, as high-quality data is…
Socratic Reasoning Improves Positive Text Rewriting
Anmol Goel, Nico Daheim, Christian Montag +1
Reframing a negative into a positive thought is at the crux of several cognitive approaches to mental health and psychotherapy that could be made more accessible by large language…
Variational Learning is Effective for Large Deep Networks
Yuesong Shen, Nico Daheim, Bai Cong +8
We give extensive empirical evidence against the common belief that variational learning is ineffective for large neural networks. We show that an optimizer called Improved Variati…