no code implementations • NAACL (Emoji) 2022 • Yunhe Feng, Cheng Guo, Bingbing Wen, Peng Sun, Yufei Yue, Dingwen Tao
This paper proposes EmojiCloud, an open-source Python-based emoji cloud visualization tool, to generate a quick and straightforward understanding of emojis from the perspective of frequency and importance.
no code implementations • 27 May 2024 • Robert Wolfe, Isaac Slaughter, Bin Han, Bingbing Wen, Yiwei Yang, Lucas Rosenblatt, Bernease Herman, Eva Brown, Zening Qu, Nic Weber, Bill Howe
The rapid proliferation of generative AI has raised questions about the competitiveness of lower-parameter, locally tunable, open-weight models relative to high-parameter, API-guarded, closed-weight models in terms of performance, domain adaptation, cost, and generalization.
1 code implementation • 18 Apr 2024 • Bingbing Wen, Bill Howe, Lucy Lu Wang
The correct model response in the face of uncertainty is to abstain from answering a question so as not to mislead the user.
no code implementations • 21 Dec 2023 • Bingbing Wen, Zhengyuan Yang, JianFeng Wang, Zhe Gan, Bill Howe, Lijuan Wang
In this paper, we build a visual dialogue dataset, named InfoVisDial, which provides rich informative answers in each round even with external knowledge related to the visual content.
no code implementations • 30 Nov 2023 • Zhangsihao Yang, Mingyuan Zhou, Mengyi Shan, Bingbing Wen, Ziwei Xuan, Mitch Hill, Junjie Bai, Guo-Jun Qi, Yalin Wang
Our paper aims to generate diverse and realistic animal motion sequences from textual descriptions, without a large-scale animal text-motion dataset.
no code implementations • 17 Aug 2022 • Bingbing Wen, Xiaoning Bu, Chirag Shah
To the best of our knowledge, this is the first framework for explainable conversational recommendation on real-world datasets.
no code implementations • 17 Aug 2022 • Bingbing Wen, Yunhe Feng, Yongfeng Zhang, Chirag Shah
Current explanation generation models are found to exaggerate certain emotions without accurately capturing the underlying tone or the meaning.