LLM-Generated Black-box Explanations Can Be Adversarially Helpful

10 May 2024

Papers citing "LLM-Generated Black-box Explanations Can Be Adversarially Helpful"

5 / 5 papers shown

Title
REV: Information-Theoretic Evaluation of Free-Text Rationales Hanjie Chen Faeze Brahman Xiang Ren Yangfeng Ji Yejin Choi Swabha Swayamdipta 79 22 0 10 Oct 2022
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 216 327 0 23 Aug 2022
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models Jason W. Wei Xuezhi Wang Dale Schuurmans Maarten Bosma Brian Ichter F. Xia Ed H. Chi Quoc Le Denny Zhou LM&Ro LRM AI4CE ReLM 315 8,261 0 28 Jan 2022
Explainability Pitfalls: Beyond Dark Patterns in Explainable AI Upol Ehsan Mark O. Riedl XAI SILM 44 56 0 26 Sep 2021
Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI Alon Jacovi Ana Marasović Tim Miller Yoav Goldberg 236 417 0 15 Oct 2020