8 citations · 8 across the 8 of their papers we have counts for
Showing 2026Show all
3 papers · 1 filter
cs.LG2026
Interpretability Can Be Actionable
Hadas Orgad, Fazl Barez, Tal Haklay +9
Interpretability aims to explain the behavior of deep neural networks. Despite rapid growth, there is mounting concern that much of this work has not translated into practical impa…
cs.CY2026
Rigorous Interpretation Is a Form of Evaluation
Isabelle Lee, Emmy Liu, Cathy Jiao +4
Current machine learning models are evaluated through behavioral snapshots, with benchmark accuracies, win rates and outcome-based metrics. Model explanations and evaluations, howe…
cs.CL2026
What do Language Models Learn and When? The Implicit Curriculum Hypothesis
Emmy Liu, Kaiser Sun, Millicent Li +4
Large language models (LLMs) can perform remarkably complex tasks, yet the fine-grained details of how these capabilities emerge during pretraining remain poorly understood. Scalin…