63 citations · 153 across the 15 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
Active Retrieval Augmented Generation
Zhengbao Jiang, Frank F. Xu, Luyu Gao +6
Despite the remarkable ability of large language models (LMs) to comprehend and generate language, they have a tendency to hallucinate and create factually inaccurate output. Augme…
Recitation-Augmented Language Models
Zhiqing Sun, Xuezhi Wang, Yi Tay +2
We propose a new paradigm to help Large Language Models (LLMs) generate more accurate factual knowledge without retrieving from an external corpus, called RECITation-augmented gEne…