no code implementations • 23 Apr 2024 • Furui Cheng, Vilém Zouhar, Robin Shing Moon Chan, Daniel Fürst, Hendrik Strobelt, Mennatallah El-Assady
First, the generated textual counterfactuals should be meaningful and readable to users and thus can be mentally compared to draw conclusions.
no code implementations • 28 Nov 2023 • Furui Cheng, Vilém Zouhar, Simran Arora, Mrinmaya Sachan, Hendrik Strobelt, Mennatallah El-Assady
To address this challenge, we propose an interactive system that helps users gain insight into the reliability of the generated text.
no code implementations • 17 Aug 2022 • Zhihua Jin, Xingbo Wang, Furui Cheng, Chunhui Sun, Qun Liu, Huamin Qu
Since shortcuts vary in coverage, productivity, and semantic meaning, it is challenging for NLU experts to systematically understand and avoid them when creating benchmark datasets.
1 code implementation • 13 Jan 2022 • Xingbo Wang, Furui Cheng, Yong Wang, Ke Xu, Jiang Long, Hong Lu, Huamin Qu
Natural language interfaces (NLIs) provide users with a convenient way to interactively analyze data through natural language queries.
1 code implementation • 4 Aug 2021 • Furui Cheng, Dongyu Liu, Fan Du, Yanna Lin, Alexandra Zytek, Haomin Li, Huamin Qu, Kalyan Veeramachaneni
Machine learning (ML) is increasingly applied to Electronic Health Records (EHRs) to solve clinical prediction tasks.
no code implementations • 19 Aug 2020 • Furui Cheng, Yao Ming, Huamin Qu
With machine learning models being increasingly applied to various decision-making scenarios, people have spent growing efforts to make machine learning models more transparent and explainable.