Measuring and Reducing LLM Hallucination without Gold-Standard Answers

16 February 2024

Papers citing "Measuring and Reducing LLM Hallucination without Gold-Standard Answers"

8 / 8 papers shown

Title
Human-Instruction-Free LLM Self-Alignment with Limited Samples Hongyi Guo Yuanshun Yao Wei Shen Jiaheng Wei Xiaoying Zhang Zhaoran Wang Yang Liu 56 10 0 06 Jan 2024
SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models Potsawee Manakul Adian Liusie Mark J. F. Gales HILM LRM 116 217 0 15 Mar 2023
Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned Deep Ganguli Liane Lovitt John Kernion Amanda Askell Yuntao Bai ... Nicholas Joseph Sam McCandlish C. Olah Jared Kaplan Jack Clark 185 327 0 23 Aug 2022
Large Language Models are Zero-Shot Reasoners Takeshi Kojima S. Gu Machel Reid Yutaka Matsuo Yusuke Iwasawa ReLM LRM 271 2,712 0 24 May 2022
Self-Consistency Improves Chain of Thought Reasoning in Language Models Xuezhi Wang Jason W. Wei Dale Schuurmans Quoc Le Ed H. Chi Sharan Narang Aakanksha Chowdhery Denny Zhou ReLM BDL LRM AI4CE 247 2,029 0 21 Mar 2022
Training language models to follow instructions with human feedback Long Ouyang Jeff Wu Xu Jiang Diogo Almeida Carroll L. Wainwright ... Amanda Askell Peter Welinder Paul Christiano Jan Leike Ryan J. Lowe OSLM ALM 270 8,441 0 04 Mar 2022
Detecting Corrupted Labels Without Training a Model to Predict Zhaowei Zhu Zihao Dong Yang Liu NoLa 117 51 0 12 Oct 2021
Are We Modeling the Task or the Annotator? An Investigation of Annotator Bias in Natural Language Understanding Datasets Mor Geva Yoav Goldberg Jonathan Berant 205 306 0 21 Aug 2019