565 citations · 818 across the 51 of their papers we have counts for
5 papers · 1 filter
LPDS: Evaluating LLM Robustness Through Logic-Preserving Difficulty Scaling
Philipp Mondorf, Samuel J. Bell, Jesse Dodge +1
As large language models (LLMs) are increasingly deployed to perform tasks with minimal human oversight, it is crucial that these models operate robustly. In particular, a model th…
Compute Optimal Scaling of Skills: Knowledge vs Reasoning
Nicholas Roberts, Niladri Chatterji, Sharan Narang +2
Scaling laws are a critical component of the LLM development pipeline, most famously as a way to forecast training decisions such as 'compute-optimally' trading-off parameter count…
Quantifying Variance in Evaluation Benchmarks
Lovish Madaan, Aaditya K. Singh, Rylan Schaeffer +5
Evaluation benchmarks are the cornerstone of measuring capabilities of large language models (LLMs), as well as driving progress in said capabilities. Originally designed to make c…
Curriculum Learning with Adam: The Devil Is in the Wrong Details
Lucas Weber, Jaap Jumelet, Paul Michel +2
Curriculum learning (CL) posits that machine learning models -- similar to humans -- may learn more efficiently from data that match their current learning progress. However, CL me…
Location Attention for Extrapolation to Longer Sequences
Yann Dubois, Gautier Dagan, Dieuwke Hupkes +1
Neural networks are surprisingly good at interpolating and perform remarkably well when the training set examples resemble those in the test set. However, they are often unable to…