1 code implementation • 30 Sep 2024 • Zhenwei Tang, Difan Jiao, Reid McIlroy-Young, Jon Kleinberg, Siddhartha Sen, Ashton Anderson
Previous work in modeling human decision-making in chess uses completely independent models to capture human style at different skill levels, meaning they lack coherence in their ability to adapt to the full spectrum of human improvement and are ultimately limited in their effectiveness as AI partners and teaching tools.
1 code implementation • 27 Nov 2023 • Difan Jiao, Yilun Liu, Zhenwei Tang, Daniel Matter, Jürgen Pfeffer, Ashton Anderson
Among the many tasks that Large Language Models (LLMs) have revolutionized is text classification.
no code implementations • 14 Jun 2023 • Griffin Floto, Mohammad Mahdi Abdollah Pour, Parsa Farinneya, Zhenwei Tang, Ali Pesaranghader, Manasa Bharadwaj, Scott Sanner
Text detoxification is a conditional text generation task aiming to remove offensive content from toxic text.
no code implementations • 9 Jun 2023 • Armin Toroghi, Griffin Floto, Zhenwei Tang, Scott Sanner
This work enables a new paradigm for using rich knowledge content and reasoning over indirect evidence as a mechanism for critiquing interactions with CRS.
1 code implementation • 23 Apr 2023 • Zhenwei Tang, Griffin Floto, Armin Toroghi, Shichao Pei, Xiangliang Zhang, Scott Sanner
In this work, we formulate the problem of recommendation with users' logical requirements (LogicRec) and construct benchmark datasets for LogicRec.
1 code implementation • 16 Aug 2022 • Tilman Hinnerichs, Zhenwei Tang, Xi Peng, Xiangliang Zhang, Robert Hoehndorf
Ontologies are one of the richest sources of knowledge.
no code implementations • 30 Jun 2022 • Shuokai Li, Yongchun Zhu, Ruobing Xie, Zhenwei Tang, Zhao Zhang, Fuzhen Zhuang, Qing He, Hui Xiong
In this paper, we propose two key points for CRS to improve the user experience: (1) Speaking like a human, human can speak with different styles according to the current dialogue context.
no code implementations • 29 May 2022 • Zhenwei Tang, Shichao Pei, Xi Peng, Fuzhen Zhuang, Xiangliang Zhang, Robert Hoehndorf
Neural logical reasoning (NLR) is a fundamental task to explore such knowledge bases, which aims at answering multi-hop queries with logical operations based on distributed representations of queries and answers.
no code implementations • 2 May 2022 • Zhenwei Tang, Shichao Pei, Zhao Zhang, Yongchun Zhu, Fuzhen Zhuang, Robert Hoehndorf, Xiangliang Zhang
Most real-world knowledge graphs (KG) are far from complete and comprehensive.
no code implementations • 28 Feb 2022 • Xi Peng, Zhenwei Tang, Maxat Kulmanov, Kexin Niu, Robert Hoehndorf
Since the intersection of boxes remains as a box, the intersectional closure is satisfied.
1 code implementation • 21 Oct 2021 • Yongchun Zhu, Zhenwei Tang, Yudan Liu, Fuzhen Zhuang, Ruobing Xie, Xu Zhang, Leyu Lin, Qing He
Specifically, a meta network fed with users' characteristic embeddings is learned to generate personalized bridge functions to achieve personalized transfer of preferences for each user.
no code implementations • 21 Jul 2020 • Jingbo Zhou, Zhenwei Tang, Min Zhao, Xiang Ge, Fuzhen Zhuang, Meng Zhou, Liming Zou, Chenglei Yang, Hui Xiong
A mobile app interface usually consists of a set of user interface modules.