7 citations · 14 across the 19 of their papers we have counts for
Showing 2026 · cs.AIShow all
2 papers · 2 filters
cs.AI2026
Neurosymbolic Alignment for Physiologically-Safe Clinical Language Models
Abdulhady Abas Abdullah, Erik Cambria, Milena Zivkovic
Clinical LLMs can generate recommendations that are factually plausible yet physiologically unsafe. We investigate whether safety alignment can be improved by grounding preference…
cs.AI2026
TUR-DPO: Topology- and Uncertainty-Aware Direct Preference Optimization
Abdulhady Abas Abdullah, Fatemeh Daneshfar, Seyedali Mirjalili +1
Aligning large language models (LLMs) with human preferences is commonly done via reinforcement learning from human feedback (RLHF) with Proximal Policy Optimization (PPO) or, more…