Search Results for author: Yuncheng Hua

Found 13 papers, 6 papers with code

Counter-intuitive: Large Language Models Can Better Understand Knowledge Graphs Than We Thought

no code implementations18 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.

Knowledge Graphs Question Answering

RENOVI: A Benchmark Towards Remediating Norm Violations in Socio-Cultural Conversations

no code implementations17 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.

Assistive Large Language Model Agents for Socially-Aware Negotiation Dialogues

no code implementations29 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.

In-Context Learning Language Modelling +1

Let's Negotiate! A Survey of Negotiation Dialogue Systems

no code implementations18 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.

A Low-Cost, Controllable and Interpretable Task-Oriented Chatbot: With Real-World After-Sale Services as Example

no code implementations13 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.

Chatbot Task-Oriented Dialogue Systems

Formal Query Building with Query Structure Prediction for Complex Question Answering over Knowledge Base

1 code implementation8 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.

Graph Generation Question Answering

Less is More: Data-Efficient Complex Question Answering over Knowledge Bases

1 code implementation29 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.

Multi-hop Question Answering Question Answering

Few-Shot Complex Knowledge Base Question Answering via Meta Reinforcement Learning

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

Cannot find the paper you are looking for? You can Submit a new open access paper.