Showing cs.CLShow all
3 papers · 1 filter
cs.CL2026
What Is Missing: Interpretable Ratings for Large Language Model Outputs
Nicholas Stranges, Yimin Yang
Current Large Language Model (LLM) preference learning methods such as Proximal Policy Optimization and Direct Preference Optimization learn from direct rankings or numerical ratin…
cs.CL2024
HaluEval-Wild: Evaluating Hallucinations of Language Models in the Wild
Zhiying Zhu, Yiming Yang, Zhiqing Sun
Hallucinations pose a significant challenge to the reliability of large language models (LLMs) in critical domains. Recent benchmarks designed to assess LLM hallucinations within c…
cs.CL2024
SALMON: Self-Alignment with Instructable Reward Models
Zhiqing Sun, Yikang Shen, Hongxin Zhang +5
Supervised Fine-Tuning (SFT) on response demonstrations combined with Reinforcement Learning from Human Feedback (RLHF) constitutes a powerful paradigm for aligning LLM-based AI ag…