Existing conversational systems tend to generate generic responses. Recently, Background Based Conversations (BBCs) have been introduced to address this issue. Here, the generated responses are grounded in some background information. The proposed methods for BBCs are able to generate more informative responses, they either cannot generate natural responses or have difficulty in locating the right background information. In this paper, we propose a Reference-aware Network (RefNet) to address the two issues. Unlike existing methods that generate responses token by token, RefNet incorporates a novel reference decoder that provides an alternative way to learn to directly cite a semantic unit (e.g., a span containing complete semantic information) from the background. Experimental results show that RefNet significantly outperforms state-of-the-art methods in terms of both automatic and human evaluations, indicating that RefNet can generate more appropriate and human-like responses.
J-CRe3: A Japanese Conversation Dataset for Real-world Reference Resolution
Kyoto University（京都大学）；RIKEN（理化学研究所）；Tokyo University of Science（东京理科大学）；Nara Institute of Science and Technology（奈良科学技术研究所）；National Institute of Informatics（情报通信研究机构）
TAF-MED: Multi-Turn Safety Refusal Collapse in LLMs Under Declared Self-Treatment Intent
Independent Researcher（独立研究者）；School of Computation, Information and Technology, Technical University of Munich（慕尼黑工业大学计算、信息与技术学院）；Institute of General Practice, Faculty of Medicine and Medical Center, University of Freiburg（弗莱堡大学医学中心医学院普通实践研究所）
There Is No Standard Answer: Knowledge-Grounded Dialogue Generation with Adversarial Activated Multi-Reference Learning
Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机研究所）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Microsoft Corporation（微软公司）
