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 • 17 Feb 2024 • Minh-Vuong Nguyen, Linhao Luo, Fatemeh Shiri, Dinh Phung, Yuan-Fang Li, Thuy-Trang Vu, Gholamreza Haffari
Large language models (LLMs) demonstrate strong reasoning abilities when prompted to generate chain-of-thought (CoT) explanations alongside answers.
no code implementations • 2 Feb 2024 • Tongtong Wu, Linhao Luo, Yuan-Fang Li, Shirui Pan, Thuy-Trang Vu, Gholamreza Haffari
Large language models (LLMs) are not amenable to frequent re-training, due to high training costs arising from their massive scale.
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 • 14 Dec 2023 • Bo Xiong, Mojtaba Nayyeri, Linhao Luo, ZiHao Wang, Shirui Pan, Steffen Staab
NestE represents each atomic fact as a $1\times3$ matrix, and each nested relation is modeled as a $3\times3$ matrix that rotates the $1\times3$ atomic fact matrix through matrix multiplication.
no code implementations • 18 Oct 2023 • Linhao Luo, Thuy-Trang Vu, Dinh Phung, Gholamreza Haffari
We systematically evaluate the state-of-the-art LLMs with KGs in generic and specific domains.
1 code implementation • 2 Oct 2023 • Linhao Luo, Yuan-Fang Li, Gholamreza Haffari, Shirui Pan
In this paper, we propose a novel method called reasoning on graphs (RoG) that synergizes LLMs with KGs to enable faithful and interpretable reasoning.
1 code implementation • 4 Sep 2023 • Linhao Luo, Jiaxin Ju, Bo Xiong, Yuan-Fang Li, Gholamreza Haffari, Shirui Pan
Logical rules are essential for uncovering the logical connections between relations, which could improve reasoning performance and provide interpretable results on knowledge graphs (KGs).
1 code implementation • 4 Aug 2023 • Jiapu Wang, Boyue Wang, Meikang Qiu, Shirui Pan, Bo Xiong, Heng Liu, Linhao Luo, Tengfei Liu, Yongli Hu, BaoCai Yin, Wen Gao
Temporal characteristics are prominently evident in a substantial volume of knowledge, which underscores the pivotal role of Temporal Knowledge Graphs (TKGs) in both academia and industry.
1 code implementation • 26 Jun 2023 • Zicheng Zhao, Linhao Luo, Shirui Pan, Quoc Viet Hung Nguyen, Chen Gong
Previous methods are limited to transductive scenarios, where entities exist in the knowledge graphs, so they are unable to handle unseen entities.
no code implementations • 14 Jun 2023 • Shirui Pan, Linhao Luo, YuFei Wang, Chen Chen, Jiapu Wang, Xindong Wu
In this article, we present a forward-looking roadmap for the unification of LLMs and KGs.
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
1 code implementation • 17 Apr 2023 • Linhao Luo, Yuan-Fang Li, Gholamreza Haffari, Shirui Pan
In this paper, we propose a normalizing flow-based neural process for few-shot knowledge graph completion (NP-FKGC).
1 code implementation • 15 Nov 2022 • Linhao Luo, Reza Haffari, Shirui Pan
Specifically, GSNOP combines the advantage of the neural process and neural ordinary differential equation that models the link prediction on dynamic graphs as a dynamic-changing stochastic process.
no code implementations • 12 Aug 2022 • Linhao Luo, Yixiang Fang, Moli Lu, Xin Cao, Xiaofeng Zhang, Wenjie Zhang
Most of existing relevance measures focus on homogeneous networks where objects are of the same type, and a few measures are developed for heterogeneous graphs, but they often need the pre-defined meta-path.
2 code implementations • 24 Apr 2022 • Chao Lin, Zhao Li, Sheng Zhou, Shichang Hu, Jialun Zhang, Linhao Luo, Jiarun Zhang, Longtao Huang, Yuan He
Virtual try-on(VTON) aims at fitting target clothes to reference person images, which is widely adopted in e-commerce. Existing VTON approaches can be narrowly categorized into Parser-Based(PB) and Parser-Free(PF) by whether relying on the parser information to mask the persons' clothes and synthesize try-on images.
1 code implementation • 25 Feb 2022 • Linhao Luo, Yumeng Li, Buyu Gao, Shuai Tang, Sinan Wang, Jiancheng Li, Tanchao Zhu, Jiancai Liu, Zhao Li, Shirui Pan
We integrate these components into a unified framework and present MAMDR, which can be applied to any model structure to perform multi-domain recommendation.
1 code implementation • 5 Sep 2021 • Linhao Luo, Yixiang Fang, Xin Cao, Xiaofeng Zhang, Wenjie Zhang
With the surge of graph embedding mechanism, it has also been adopted to community detection.
no code implementations • 25 Nov 2020 • Linhao Luo, Liqi Yang, Ju Xin, Yixiang Fang, Xiaofeng Zhang, Xiaofei Yang, Kai Chen, Zhiyuan Zhang, Kai Liu
In particular, we technically propose a novel random CNN component that can randomly convolute non-adjacent features to capture their interaction information and learn feature embeddings of key attributes to make the final recommendation.
no code implementations • 22 Oct 2019 • Dan Peng, Zizhan Zheng, Linhao Luo, Xiaofeng Zhang
In this paper, we propose the novel concepts of structure patterns and structure-aware perturbations that relax the small perturbation constraint while still keeping images natural.