no code implementations • 21 Feb 2024 • Boyang Xue, Hongru Wang, Weichao Wang, Rui Wang, Sheng Wang, Zeming Liu, Kam-Fai Wong
The tendency of Large Language Models to generate hallucinations and exhibit overconfidence in predictions raises concerns regarding their reliability.
1 code implementation • 30 Dec 2023 • Can Chen, Hao liu, Zeming Liu, Xue Liu, Dejing Dou
In this paper, we propose Dual-space Hierarchical Learning (DHL) to leverage multi-level goal sequences and their hierarchical relationships for conversational recommendation.
1 code implementation • 5 Jun 2023 • Xiaoming Shi, Zeming Liu, Chuan Wang, Haitao Leng, Kui Xue, Xiaofan Zhang, Shaoting Zhang
To mitigate this challenge, we propose a novel task and create a human-to-human mixed-type medical consultation dialogue corpus, termed MidMed, covering five dialogue types: task-oriented dialogue for diagnosis, recommendation, knowledge-grounded dialogue, QA, and chitchat.
1 code implementation • 10 Nov 2022 • Mo Wang, Kexin Lou, Zeming Liu, Pengfei Wei, Quanying Liu
In this paper, we propose a general framework called multi-objective optimization via evolutionary algorithms (MOVEA) to address the non-convex optimization problem in designing TES strategies without predefined direction.
no code implementations • ACL 2022 • Zeming Liu, Jun Xu, Zeyang Lei, Haifeng Wang, Zheng-Yu Niu, Hua Wu
For example, users have determined the departure, the destination, and the travel time for booking a flight.
1 code implementation • EMNLP 2021 • Zeming Liu, Haifeng Wang, Zheng-Yu Niu, Hua Wu, Wanxiang Che
In this paper, we provide a bilingual parallel human-to-human recommendation dialog dataset (DuRecDial 2. 0) to enable researchers to explore a challenging task of multilingual and cross-lingual conversational recommendation.
2 code implementations • ACL 2020 • Zeming Liu, Haifeng Wang, Zheng-Yu Niu, Hua Wu, Wanxiang Che, Ting Liu
We propose a new task of conversational recommendation over multi-type dialogs, where the bots can proactively and naturally lead a conversation from a non-recommendation dialog (e. g., QA) to a recommendation dialog, taking into account user's interests and feedback.