1 citations · 1 across the 3 of their papers we have counts for
4 papers
The Assistance Dilemma: Learning to Teach via Multi-Turn Reinforcement Learning
Jakub Macina, Manu Kapur, Mrinmaya Sachan
Large language models (LLMs) trained to answer questions are natively poor at teaching. Reinforcement Learning (RL) against a simulated student is a promising approach to improve t…
MathTutorBench: A Benchmark for Measuring Open-ended Pedagogical Capabilities of LLM Tutors
Jakub Macina, Nico Daheim, Ido Hakimi +3
Evaluating the pedagogical capabilities of AI-based tutoring models is critical for making guided progress in the field. Yet, we lack a reliable, easy-to-use, and simple-to-run eva…
Towards the Pedagogical Steering of Large Language Models for Tutoring: A Case Study with Modeling Productive Failure
Romain Puech, Jakub Macina, Julia Chatain +2
One-to-one tutoring is one of the most efficient methods of teaching. With the growing popularity of Large Language Models (LLMs), there have been efforts to create LLM based conve…
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…