128 citations · 137 across the 4 of their papers we have counts for
4 papers
When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards
Norah Alzahrani, Hisham Abdullah Alyahya, Yazeed Alnumay +9
Large Language Model (LLM) leaderboards based on benchmark rankings are regularly used to guide practitioners in model selection. Often, the published leaderboard rankings are take…
Holistic Evaluation of Language Models
Percy Liang, Rishi Bommasani, Tony Lee +47
Language models (LMs) are becoming the foundation for almost all major language technologies, but their capabilities, limitations, and risks are not well understood. We present Hol…
FORML: Learning to Reweight Data for Fairness
Bobby Yan, Skyler Seto, Nicholas Apostoloff
Machine learning models are trained to minimize the mean loss for a single metric, and thus typically do not consider fairness and robustness. Neglecting such metrics in training c…
Hindsight Logging for Model Training
Rolando Garcia, Eric Liu, Vikram Sreekanti +5
In modern Machine Learning, model training is an iterative, experimental process that can consume enormous computation resources and developer time. To aid in that process, experie…