1 code implementation • 10 Sep 2024 • Yuan Tian, Jonathan K. Kummerfeld, Toby Jia-Jun Li, Tianyi Zhang
Though recent advances in machine learning have led to significant improvements in natural language interfaces for databases, the accuracy and reliability of these systems remain limited, especially in high-stakes domains.
no code implementations • 7 Aug 2024 • Simret Araya Gebreegziabher, Kuangshi Ai, Zheng Zhang, Elena L. Glassman, Toby Jia-Jun Li
Active Learning (AL) allows models to learn interactively from user feedback.
no code implementations • 21 May 2024 • Ruyuan Wan, Simret Gebreegziabhe, Toby Jia-Jun Li, Karla Badillo-Urquiola
To understand how to recognize human efforts in co-writing with intelligent writing systems, we adapt Flower and Hayes' cognitive process theory of writing and propose CoCo Matrix, a two-dimensional taxonomy of entropy and information gain, to depict the new human-agent co-writing model.
1 code implementation • 21 Feb 2024 • Yifan Zhang, Jiliang Li, Zachary Karas, Aakash Bansal, Toby Jia-Jun Li, Collin McMillan, Kevin Leach, Yu Huang
Neural code summarization leverages deep learning models to automatically generate brief natural language summaries of code snippets.
no code implementations • 16 Nov 2023 • Yuxuan Lu, Bingsheng Yao, Shao Zhang, Yun Wang, Peng Zhang, Tun Lu, Toby Jia-Jun Li, Dakuo Wang
Large Language Models (LLMs) have demonstrated considerable advances, and several claims have been made about their exceeding human performance.
no code implementations • 24 Oct 2023 • Yuwen Lu, Ziang Tong, Qinyi Zhao, Chengzhi Zhang, Toby Jia-Jun Li
The recent advances in Large Language Models (LLMs) have stimulated interest among researchers and industry professionals, particularly in their application to tasks concerning mobile user interfaces (UIs).
no code implementations • 19 Oct 2023 • Sangho Suh, Meng Chen, Bryan Min, Toby Jia-Jun Li, Haijun Xia
To address this limitation, we propose a framework that facilitates the structured generation of design space in which users can seamlessly explore, evaluate, and synthesize a multitude of responses.
1 code implementation • 29 Aug 2023 • Hao Wen, Yuanchun Li, Guohong Liu, Shanhui Zhao, Tao Yu, Toby Jia-Jun Li, Shiqi Jiang, Yunhao Liu, Yaqin Zhang, Yunxin Liu
Mobile task automation is an attractive technique that aims to enable voice-based hands-free user interaction with smartphones.
1 code implementation • 12 May 2023 • Yuan Tian, Zheng Zhang, Zheng Ning, Toby Jia-Jun Li, Jonathan K. Kummerfeld, Tianyi Zhang
Many techniques have been proposed to automatically generate SQL from natural language, but they suffer from two issues: (1) they still make many mistakes, particularly for complex queries, and (2) they do not provide a flexible way for non-expert users to validate and refine incorrect queries.
no code implementations • 16 Apr 2023 • Zheng Zhang, Jie Gao, Ranjodh Singh Dhaliwal, Toby Jia-Jun Li
In argumentative writing, writers must brainstorm hierarchical writing goals, ensure the persuasiveness of their arguments, and revise and organize their plans through drafting.
1 code implementation • 6 Oct 2022 • Shao Zhang, Yuting Jia, Hui Xu, Dakuo Wang, Toby Jia-Jun Li, Ying Wen, Xinbing Wang, Chenghu Zhou
Constructing a comprehensive, accurate, and useful scientific knowledge base is crucial for human researchers synthesizing scientific knowledge and for enabling Al-driven scientific discovery.
no code implementations • 29 Apr 2022 • Toby Jia-Jun Li, Yuwen Lu, Jaylexia Clark, Meng Chen, Victor Cox, Meng Jiang, Yang Yang, Tamara Kay, Danielle Wood, Jay Brockman
The AI inequality is caused by (1) the technology divide in who has access to AI technologies in gig work; and (2) the data divide in who owns the data in gig work leads to unfair working conditions, growing pay gap, neglect of workers' diverse preferences, and workers' lack of trust in the platforms.
1 code implementation • 26 Mar 2022 • Ying Xu, Dakuo Wang, Mo Yu, Daniel Ritchie, Bingsheng Yao, Tongshuang Wu, Zheng Zhang, Toby Jia-Jun Li, Nora Bradford, Branda Sun, Tran Bao Hoang, Yisi Sang, Yufang Hou, Xiaojuan Ma, Diyi Yang, Nanyun Peng, Zhou Yu, Mark Warschauer
Through benchmarking with QG models, we show that the QG model trained on FairytaleQA is capable of asking high-quality and more diverse questions.
Ranked #1 on Question Generation on FairytaleQA
1 code implementation • 13 Feb 2022 • Zheng Zhang, Ying Xu, Yanhao Wang, Bingsheng Yao, Daniel Ritchie, Tongshuang Wu, Mo Yu, Dakuo Wang, Toby Jia-Jun Li
Despite its benefits for children's skill development and parent-child bonding, many parents do not often engage in interactive storytelling by having story-related dialogues with their child due to limited availability or challenges in coming up with appropriate questions.
2 code implementations • 8 Sep 2021 • Bingsheng Yao, Dakuo Wang, Tongshuang Wu, Zheng Zhang, Toby Jia-Jun Li, Mo Yu, Ying Xu
Existing question answering (QA) techniques are created mainly to answer questions asked by humans.
1 code implementation • ACL 2020 • Toby Jia-Jun Li, Tom Mitchell, Brad Myers
We show SUGILITE, an intelligent task automation agent that can learn new tasks and relevant associated concepts interactively from the user{'}s natural language instructions and demonstrations, using the graphical user interfaces (GUIs) of third-party mobile apps.
no code implementations • 17 Apr 2020 • Toby Jia-Jun Li, Jingya Chen, Brandon Canfield, Brad A. Myers
An important concern in end user development (EUD) is accidentally embedding personal information in program artifacts when sharing them.
no code implementations • 5 Mar 2020 • Toby Jia-Jun Li, Jingya Chen, Tom M. Mitchell, Brad A. Myers
Lastly, we identify several challenges and opportunities, and describe our ongoing work
no code implementations • 30 Aug 2019 • Toby Jia-Jun Li, Marissa Radensky, Justin Jia, Kirielle Singarajah, Tom M. Mitchell, Brad A. Myers
In this paper, we describe a new multi-modal domain-independent approach that combines natural language programming and programming-by-demonstration to allow users to first naturally describe tasks and associated conditions at a high level, and then collaborate with the agent to recursively resolve any ambiguities or vagueness through conversations and demonstrations.
Human-Computer Interaction