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cs.AI2025
LOGicalThought: Logic-Based Ontological Grounding of LLMs for High-Assurance Reasoning
Navapat Nananukul, Yue Zhang, Ryan Lee +5
High-assurance reasoning, particularly in critical domains such as law and medicine, requires conclusions that are accurate, verifiable, and explicitly grounded in evidence. This r…
cs.CL2025
A Systematic Analysis of Base Model Choice for Reward Modeling
Kian Ahrabian, Pegah Jandaghi, Negar Mokhberian +2
Reinforcement learning from human feedback (RLHF) and, at its core, reward modeling have become a crucial part of training powerful large language models (LLMs). One commonly overl…