Chat as Expected: Learning to Manipulate Black-box Neural Dialogue Models

27 May 2020

Papers citing "Chat as Expected: Learning to Manipulate Black-box Neural Dialogue Models"

9 / 9 papers shown

Title
Safer Conversational AI as a Source of User Delight Xiaoding Lu Aleksey Korshuk Z. Liu W. Beauchamp Chai Research 23 3 0 18 Apr 2023
Constructing Highly Inductive Contexts for Dialogue Safety through Controllable Reverse Generation Zhexin Zhang Jiale Cheng Hao-Lun Sun Jiawen Deng Fei Mi Yasheng Wang Lifeng Shang Minlie Huang SILM 30 8 0 04 Dec 2022
Red Teaming Language Models with Language Models Ethan Perez Saffron Huang Francis Song Trevor Cai Roman Ring John Aslanides Amelia Glaese Nat McAleese G. Irving AAML 13 609 0 07 Feb 2022
Automatically Exposing Problems with Neural Dialog Models Dian Yu Kenji Sagae 23 9 0 14 Sep 2021
Anticipating Safety Issues in E2E Conversational AI: Framework and Tooling Emily Dinan Gavin Abercrombie A. S. Bergman Shannon L. Spruit Dirk Hovy Y-Lan Boureau Verena Rieser 32 105 0 07 Jul 2021
Recipes for Safety in Open-domain Chatbots Jing Xu Da Ju Margaret Li Y-Lan Boureau Jason Weston Emily Dinan 16 229 0 14 Oct 2020
Mitigating Gender Bias for Neural Dialogue Generation with Adversarial Learning Haochen Liu Wentao Wang Yiqi Wang Hui Liu Zitao Liu Jiliang Tang 6 71 0 28 Sep 2020
Yet Meta Learning Can Adapt Fast, It Can Also Break Easily Han Xu Yaxin Li Xiaorui Liu Hui Liu Jiliang Tang AAML 21 10 0 02 Sep 2020
Does Gender Matter? Towards Fairness in Dialogue Systems Haochen Liu Jamell Dacon Wenqi Fan Hui Liu Zitao Liu Jiliang Tang 25 141 0 16 Oct 2019