2 citations · 2 across the 2 of their papers we have counts for
2 papers
cs.CL2025
IDEAlign: Comparing Ideas of Large Language Models to Domain Expert
Hyunji Nam, Lucia Langlois, James Malamut +2
Large language models (LLMs) are increasingly used to produce open-ended, interpretive annotations, yet there is no validated, scalable measure of idea-level similarity to expert a…
cs.CY2023★ 2 cited
Is a Seat at the Table Enough? Engaging Teachers and Students in Dataset Specification for ML in Education
Mei Tan, Hansol Lee, Dakuo Wang +1
Despite the promises of ML in education, its adoption in the classroom has surfaced numerous issues regarding fairness, accountability, and transparency, as well as concerns about…