19 citations · 27 across the 9 of their papers we have counts for
3 papers · 1 filter
Fantastic Bugs and Where to Find Them in AI Benchmarks
Sang Truong, Yuheng Tu, Michael Hardy +8
Benchmarks are pivotal in driving AI progress, and invalid benchmark questions frequently undermine their reliability. Manually identifying and correcting errors among thousands of…
Assessing the Quality of AI-Generated Exams: A Large-Scale Field Study
Calvin Isley, Joshua Gilbert, Evangelos Kassos +9
While large language models (LLMs) challenge conventional methods of teaching and learning, they present an exciting opportunity to improve efficiency and scale high-quality instru…
Measurement to Meaning: A Validity-Centered Framework for AI Evaluation
Olawale Salaudeen, Anka Reuel, Ahmed Ahmed +6
While the capabilities and utility of AI systems have advanced, rigorous norms for evaluating these systems have lagged. Grand claims, such as models achieving general reasoning ca…