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 Sun Jiawen Deng Fei Mi Yasheng Wang Lifeng Shang Minlie Huang SILM 32 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 31 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 37 105 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 19 5 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