1 code implementation • 3 Oct 2024 • Romain Puech, Jakub Macina, Julia Chatain, Mrinmaya Sachan, Manu Kapur
To use LLMs in pedagogical scenarios, they need to be steered towards using effective teaching strategies: a problem we introduce as Pedagogical Steering and believe to be crucial for the efficient use of LLMs as tutors.
1 code implementation • 12 Jul 2024 • Nico Daheim, Jakub Macina, Manu Kapur, Iryna Gurevych, Mrinmaya Sachan
We show empirically that finding the mistake in a student solution is challenging for current models.
no code implementations • 5 Mar 2024 • Junling Wang, Jakub Macina, Nico Daheim, Sankalan Pal Chowdhury, Mrinmaya Sachan
Educational chatbots are a promising tool for assisting student learning.
1 code implementation • 23 May 2023 • Jakub Macina, Nico Daheim, Sankalan Pal Chowdhury, Tanmay Sinha, Manu Kapur, Iryna Gurevych, Mrinmaya Sachan
While automatic dialogue tutors hold great potential in making education personalized and more accessible, research on such systems has been hampered by a lack of sufficiently large and high-quality datasets.
1 code implementation • 24 Jan 2023 • Jakub Macina, Nico Daheim, Lingzhi Wang, Tanmay Sinha, Manu Kapur, Iryna Gurevych, Mrinmaya Sachan
Designing dialog tutors has been challenging as it involves modeling the diverse and complex pedagogical strategies employed by human tutors.
1 code implementation • 23 Nov 2022 • Kumar Shridhar, Jakub Macina, Mennatallah El-Assady, Tanmay Sinha, Manu Kapur, Mrinmaya Sachan
On both automatic and human quality evaluations, we find that LMs constrained with desirable question properties generate superior questions and improve the overall performance of a math word problem solver.
no code implementations • 13 Mar 2022 • Michal Kompan, Peter Gaspar, Jakub Macina, Matus Cimerman, Maria Bielikova
We propose an adjustment of a predicted ranking for score-based recommender systems and explore the effect of the profit and customers' price preferences on two industry datasets from the fashion domain.