2 citations · 2 across the 1 of their papers we have counts for
3 papers
How RLHF Amplifies Sycophancy
Itai Shapira, Gerdus Benade, Ariel D. Procaccia
Large language models often exhibit increased sycophantic behavior after preference-based post-training, showing a stronger tendency to affirm a user's stated or implied belief eve…
Incentives in Federated Learning with Heterogeneous Agents
Ariel D. Procaccia, Han Shao, Itai Shapira
Federated learning promises significant sample-efficiency gains by pooling data across multiple agents, yet incentive misalignment is an obstacle: each update is costly to the cont…
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…