1 code implementation • 4 Mar 2024 • YiFan Song, Da Yin, Xiang Yue, Jie Huang, Sujian Li, Bill Yuchen Lin
This iterative cycle of exploration and training fosters continued improvement in the agents.
1 code implementation • 25 Feb 2024 • Fanjin Zhang, Kun Cao, Yukuo Cen, Jifan Yu, Da Yin, Jie Tang
Tracing the source of research papers is a fundamental yet challenging task for researchers.
1 code implementation • 9 Nov 2023 • Da Yin, Faeze Brahman, Abhilasha Ravichander, Khyathi Chandu, Kai-Wei Chang, Yejin Choi, Bill Yuchen Lin
To foster generalizable agent learning, we collect large-scale, unified, and high-quality training annotations derived from diverse ground-truth reasoning rationales across various complex interactive tasks.
no code implementations • 16 Oct 2023 • Christina Chance, Da Yin, Dakuo Wang, Kai-Wei Chang
Using counterfactual data augmentation to the FairytaleQA dataset, we evaluate model robustness against swapped gender character information, and then mitigate learned biases by introducing counterfactual gender stereotypes during training time.
1 code implementation • 30 May 2023 • Xiao Liu, Da Yin, Chen Zhang, Yansong Feng, Dongyan Zhao
Causal reasoning, the ability to identify cause-and-effect relationship, is crucial in human thinking.
1 code implementation • 23 May 2023 • Da Yin, Xiao Liu, Fan Yin, Ming Zhong, Hritik Bansal, Jiawei Han, Kai-Wei Chang
Instruction tuning has emerged to enhance the capabilities of large language models (LLMs) to comprehend instructions and generate appropriate responses.
1 code implementation • 27 Mar 2023 • Di wu, Da Yin, Kai-Wei Chang
Despite the significant advancements in keyphrase extraction and keyphrase generation methods, the predominant approach for evaluation mainly relies on exact matching with human references.
no code implementations • CVPR 2023 • Da Yin, Feng Gao, Govind Thattai, Michael Johnston, Kai-Wei Chang
A key goal for the advancement of AI is to develop technologies that serve the needs not just of one group but of all communities regardless of their geographical region.
1 code implementation • 27 Oct 2022 • Hritik Bansal, Da Yin, Masoud Monajatipoor, Kai-Wei Chang
To this end, we introduce an Ethical NaTural Language Interventions in Text-to-Image GENeration (ENTIGEN) benchmark dataset to evaluate the change in image generations conditional on ethical interventions across three social axes -- gender, skin color, and culture.
Cultural Vocal Bursts Intensity Prediction Text-to-Image Generation
2 code implementations • 13 Oct 2022 • Ming Zhong, Yang Liu, Da Yin, Yuning Mao, Yizhu Jiao, PengFei Liu, Chenguang Zhu, Heng Ji, Jiawei Han
We re-frame NLG evaluation as a Boolean Question Answering (QA) task, and by guiding the model with different questions, we can use one evaluator to evaluate from multiple dimensions.
1 code implementation • 24 May 2022 • Da Yin, Hritik Bansal, Masoud Monajatipoor, Liunian Harold Li, Kai-Wei Chang
In this paper, we introduce a benchmark dataset, Geo-Diverse Commonsense Multilingual Language Models Analysis (GeoMLAMA), for probing the diversity of the relational knowledge in multilingual PLMs.
1 code implementation • ACL 2022 • Xiao Liu, Da Yin, Yansong Feng, Dongyan Zhao
We probe PLMs and models with visual signals, including vision-language pretrained models and image synthesis models, on this benchmark, and find that image synthesis models are more capable of learning accurate and consistent spatial knowledge than other models.
no code implementations • 17 Feb 2022 • Da Yin, Li Dong, Hao Cheng, Xiaodong Liu, Kai-Wei Chang, Furu Wei, Jianfeng Gao
With the increasing of model capacity brought by pre-trained language models, there emerges boosting needs for more knowledgeable natural language processing (NLP) models with advanced functionalities including providing and making flexible use of encyclopedic and commonsense knowledge.
1 code implementation • 8 Nov 2021 • Qinkai Zheng, Xu Zou, Yuxiao Dong, Yukuo Cen, Da Yin, Jiarong Xu, Yang Yang, Jie Tang
To bridge this gap, we present the Graph Robustness Benchmark (GRB) with the goal of providing a scalable, unified, modular, and reproducible evaluation for the adversarial robustness of GML models.
1 code implementation • EMNLP 2021 • Da Yin, Liunian Harold Li, Ziniu Hu, Nanyun Peng, Kai-Wei Chang
Commonsense is defined as the knowledge that is shared by everyone.
Ranked #1 on Visual Commonsense Reasoning on GD-VCR
Cultural Vocal Bursts Intensity Prediction Visual Commonsense Reasoning
4 code implementations • NeurIPS 2021 • Ming Ding, Zhuoyi Yang, Wenyi Hong, Wendi Zheng, Chang Zhou, Da Yin, Junyang Lin, Xu Zou, Zhou Shao, Hongxia Yang, Jie Tang
Text-to-Image generation in the general domain has long been an open problem, which requires both a powerful generative model and cross-modal understanding.
Ranked #56 on Text-to-Image Generation on MS COCO (using extra training data)
1 code implementation • NAACL 2021 • Xiao Liu, Da Yin, Yansong Feng, Yuting Wu, Dongyan Zhao
Causal inference is the process of capturing cause-effect relationship among variables.
1 code implementation • NAACL 2021 • Ming Zhong, Da Yin, Tao Yu, Ahmad Zaidi, Mutethia Mutuma, Rahul Jha, Ahmed Hassan Awadallah, Asli Celikyilmaz, Yang Liu, Xipeng Qiu, Dragomir Radev
As increasing numbers of meetings are recorded and transcribed, meeting summaries have become essential to remind those who may or may not have attended the meetings about the key decisions made and the tasks to be completed.
1 code implementation • 19 Mar 2021 • Xu Zou, Da Yin, Qingyang Zhong, Ming Ding, Hongxia Yang, Zhilin Yang, Jie Tang
To tackle this challenge, we propose an innovative method, inverse prompting, to better control text generation.
1 code implementation • 3 Mar 2021 • Xiao Liu, Da Yin, Jingnan Zheng, Xingjian Zhang, Peng Zhang, Hongxia Yang, Yuxiao Dong, Jie Tang
Academic knowledge services have substantially facilitated the development of the science enterprise by providing a plenitude of efficient research tools.
no code implementations • ACL 2020 • Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang
Pre-trained visually grounded language models such as ViLBERT, LXMERT, and UNITER have achieved significant performance improvement on vision-and-language tasks but what they learn during pre-training remains unclear.
2 code implementations • ACL 2020 • Da Yin, Tao Meng, Kai-Wei Chang
We propose SentiBERT, a variant of BERT that effectively captures compositional sentiment semantics.
7 code implementations • 9 Aug 2019 • Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang
We propose VisualBERT, a simple and flexible framework for modeling a broad range of vision-and-language tasks.
Ranked #1 on Visual Reasoning on NLVR
no code implementations • WS 2019 • Da Yin, Xiao Liu, Xiuyu Wu, Baobao Chang
In this paper, we propose a soft label approach to target-level sentiment classification task, in which a history-based soft labeling model is proposed to measure the possibility of a context word as an opinion word.