2 citations · 3 across the 3 of their papers we have counts for
3 papers
stat.ML2024★ 1 cited
BanditCAT and AutoIRT: Machine Learning Approaches to Computerized Adaptive Testing and Item Calibration
James Sharpnack, Kevin Hao, Phoebe Mulcaire +4
In this paper, we present a complete framework for quickly calibrating and administering a robust large-scale computerized adaptive test (CAT) with a small number of responses. Cal…
cs.CL2024★ 2 cited
From Tarzan to Tolkien: Controlling the Language Proficiency Level of LLMs for Content Generation
Ali Malik, Stephen Mayhew, Chris Piech +1
We study the problem of controlling the difficulty level of text generated by Large Language Models (LLMs) for contexts where end-users are not fully proficient, such as language l…
cs.IR2024
Large Language Model Augmented Exercise Retrieval for Personalized Language Learning
Austin Xu, Will Monroe, Klinton Bicknell
We study the problem of zero-shot exercise retrieval in the context of online language learning, to give learners the ability to explicitly request personalized exercises via natur…