no code implementations • 18 Feb 2024 • Xinbang Dai, Yuncheng Hua, Tongtong Wu, Yang Sheng, Guilin Qi
Although the method of enhancing large language models' (LLMs') reasoning ability and reducing their hallucinations through the use of knowledge graphs (KGs) has received widespread attention, the exploration of how to enable LLMs to integrate the structured knowledge in KGs on-the-fly remains inadequate.
no code implementations • 17 Feb 2024 • Haolan Zhan, Zhuang Li, Xiaoxi Kang, Tao Feng, Yuncheng Hua, Lizhen Qu, Yi Ying, Mei Rianto Chandra, Kelly Rosalin, Jureynolds Jureynolds, Suraj Sharma, Shilin Qu, Linhao Luo, Lay-Ki Soon, Zhaleh Semnani Azad, Ingrid Zukerman, Gholamreza Haffari
While collecting sufficient human-authored data is costly, synthetic conversations provide suitable amounts of data to help mitigate the scarcity of training data, as well as the chance to assess the alignment between LLMs and humans in the awareness of social norms.
no code implementations • 2 Feb 2024 • Haolan Zhan, YuFei Wang, Tao Feng, Yuncheng Hua, Suraj Sharma, Zhuang Li, Lizhen Qu, Zhaleh Semnani Azad, Ingrid Zukerman, Gholamreza Haffari
Negotiation is a crucial ability in human communication.
no code implementations • 29 Jan 2024 • Yuncheng Hua, Lizhen Qu, Gholamreza Haffari
In this work, we aim to develop LLM agents to mitigate social norm violations in negotiations in a multi-agent setting.
1 code implementation • 29 Jan 2024 • Yuncheng Hua, Zhuang Li, Linhao Luo, Kadek Ananta Satriadi, Tao Feng, Haolan Zhan, Lizhen Qu, Suraj Sharma, Ingrid Zukerman, Zhaleh Semnani-Azad, Gholamreza Haffari
We have released our code and software at:~\url{https://github. com/AnonymousEACLDemo/SADAS}.
1 code implementation • 24 Apr 2023 • Haolan Zhan, Zhuang Li, YuFei Wang, Linhao Luo, Tao Feng, Xiaoxi Kang, Yuncheng Hua, Lizhen Qu, Lay-Ki Soon, Suraj Sharma, Ingrid Zukerman, Zhaleh Semnani-Azad, Gholamreza Haffari
To the best of our knowledge, SocialDial is the first socially-aware dialogue dataset that covers multiple social factors and has fine-grained labels.
Cultural Vocal Bursts Intensity Prediction Synthetic Data Generation
no code implementations • 3 Apr 2023 • Yuncheng Hua, Xiangyu Xi, Zheng Jiang, Guanwei Zhang, Chaobo Sun, Guanglu Wan, Wei Ye
End-to-end generation-based approaches have been investigated and applied in task-oriented dialogue systems.
no code implementations • 18 Dec 2022 • Haolan Zhan, YuFei Wang, Tao Feng, Yuncheng Hua, Suraj Sharma, Zhuang Li, Lizhen Qu, Gholamreza Haffari
Negotiation is one of the crucial abilities in human communication, and there has been a resurgent research interest in negotiation dialogue systems recently, which goal is to empower intelligent agents with such ability that can efficiently help humans resolve conflicts or reach beneficial agreements.
no code implementations • 13 May 2022 • Xiangyu Xi, Chenxu Lv, Yuncheng Hua, Wei Ye, Chaobo Sun, Shuaipeng Liu, Fan Yang, Guanglu Wan
Though widely used in industry, traditional task-oriented dialogue systems suffer from three bottlenecks: (i) difficult ontology construction (e. g., intents and slots); (ii) poor controllability and interpretability; (iii) annotation-hungry.
1 code implementation • 8 Sep 2021 • Yongrui Chen, Huiying Li, Yuncheng Hua, Guilin Qi
However, this candidate generation strategy ignores the structure of queries, resulting in a considerable number of noisy queries.
1 code implementation • 29 Oct 2020 • Yuncheng Hua, Yuan-Fang Li, Guilin Qi, Wei Wu, Jingyao Zhang, Daiqing Qi
Our framework consists of a neural generator and a symbolic executor that, respectively, transforms a natural-language question into a sequence of primitive actions, and executes them over the knowledge base to compute the answer.
1 code implementation • EMNLP 2020 • Yuncheng Hua, Yuan-Fang Li, Gholamreza Haffari, Guilin Qi, Tongtong Wu
Our method achieves state-of-the-art performance on the CQA dataset (Saha et al., 2018) while using only five trial trajectories for the top-5 retrieved questions in each support set, and metatraining on tasks constructed from only 1% of the training set.
Knowledge Base Question Answering Meta Reinforcement Learning +3
1 code implementation • 29 Oct 2020 • Yuncheng Hua, Yuan-Fang Li, Gholamreza Haffari, Guilin Qi, Wei Wu
However, this comes at the cost of manually labeling similar questions to learn a retrieval model, which is tedious and expensive.