3 citations · 7 across the 10 of their papers we have counts for
4 papers · 1 filter
What Is Lost in Post-Training? Default Collapse and the Loss of In-Context Steerability Across Diverse Perspectives
Jessica Dierking, Itai Shapira, Niclas Boehmer
AI models serving a heterogeneous population must act on the principles appropriate to each user and context. While post-training has been shown to narrow the views large language…
Robust AI Evaluation through Maximal Lotteries
Hadi Khalaf, Serena L. Wang, Daniel Halpern +3
The standard way to evaluate language models on subjective tasks is through pairwise comparisons: an annotator chooses the "better" of two responses to a prompt. Leaderboards aggre…
Pairwise Calibrated Rewards for Pluralistic Alignment
Daniel Halpern, Evi Micha, Ariel D. Procaccia +1
Current alignment pipelines presume a single, universal notion of desirable behavior. However, human preferences often diverge across users, contexts, and cultures. As a result, di…
Expressivity of Shallow and Deep Neural Networks for Polynomial Approximation
Itai Shapira
This study explores the number of neurons required for a Rectified Linear Unit (ReLU) neural network to approximate multivariate monomials. We establish an exponential lower bound…