1 code implementation • ACL 2022 • Saadia Gabriel, Skyler Hallinan, Maarten Sap, Pemi Nguyen, Franziska Roesner, Eunsol Choi, Yejin Choi
Even to a simple and short news headline, readers react in a multitude of ways: cognitively (e. g. inferring the writer’s intent), emotionally (e. g. feeling distrust), and behaviorally (e. g. sharing the news with their friends).
no code implementations • 14 Feb 2024 • Salman Rahman, Lavender Yao Jiang, Saadia Gabriel, Yindalon Aphinyanaphongs, Eric Karl Oermann, Rumi Chunara
Overall, this study provides new insights for enhancing the deployment of large language models in the societally important domain of healthcare, and improving their performance for broader populations.
no code implementations • 31 Aug 2023 • Katherine Deng, Arijit Ray, Reuben Tan, Saadia Gabriel, Bryan A. Plummer, Kate Saenko
We further see that current captioning metrics based on large vision-language models also fail to correlate with human preferences.
1 code implementation • 26 Jul 2023 • Xuhai Xu, Bingsheng Yao, Yuanzhe Dong, Saadia Gabriel, Hong Yu, James Hendler, Marzyeh Ghassemi, Anind K. Dey, Dakuo Wang
More importantly, our experiments show that instruction finetuning can significantly boost the performance of LLMs for all tasks simultaneously.
no code implementations • 8 Nov 2022 • Saadia Gabriel, Hamid Palangi, Yejin Choi
While a substantial body of prior work has explored adversarial example generation for natural language understanding tasks, these examples are often unrealistic and diverge from the real-world data distributions.
1 code implementation • ACL 2022 • Thomas Hartvigsen, Saadia Gabriel, Hamid Palangi, Maarten Sap, Dipankar Ray, Ece Kamar
To help mitigate these issues, we create ToxiGen, a new large-scale and machine-generated dataset of 274k toxic and benign statements about 13 minority groups.
1 code implementation • 14 Oct 2021 • Liwei Jiang, Jena D. Hwang, Chandra Bhagavatula, Ronan Le Bras, Jenny Liang, Jesse Dodge, Keisuke Sakaguchi, Maxwell Forbes, Jon Borchardt, Saadia Gabriel, Yulia Tsvetkov, Oren Etzioni, Maarten Sap, Regina Rini, Yejin Choi
As AI systems become increasingly powerful and pervasive, there are growing concerns about machines' morality or a lack thereof.
1 code implementation • 18 Apr 2021 • Saadia Gabriel, Skyler Hallinan, Maarten Sap, Pemi Nguyen, Franziska Roesner, Eunsol Choi, Yejin Choi
We propose Misinfo Reaction Frames (MRF), a pragmatic formalism for modeling how readers might react to a news headline.
no code implementations • Findings (ACL) 2021 • Saadia Gabriel, Asli Celikyilmaz, Rahul Jha, Yejin Choi, Jianfeng Gao
While neural language models can generate text with remarkable fluency and coherence, controlling for factual correctness in generation remains an open research question.
1 code implementation • 4 Oct 2020 • Saadia Gabriel, Chandra Bhagavatula, Vered Shwartz, Ronan Le Bras, Maxwell Forbes, Yejin Choi
Human understanding of narrative texts requires making commonsense inferences beyond what is stated explicitly in the text.
no code implementations • 24 Apr 2020 • Zezhou Cheng, Saadia Gabriel, Pankaj Bhambhani, Daniel Sheldon, Subhransu Maji, Andrew Laughlin, David Winkler
The US weather radar archive holds detailed information about biological phenomena in the atmosphere over the last 20 years.
no code implementations • ACL 2020 • Maarten Sap, Saadia Gabriel, Lianhui Qin, Dan Jurafsky, Noah A. Smith, Yejin Choi
We introduce Social Bias Frames, a new conceptual formalism that aims to model the pragmatic frames in which people project social biases and stereotypes onto others.
no code implementations • EACL 2021 • Saadia Gabriel, Antoine Bosselut, Jeff Da, Ari Holtzman, Jan Buys, Kyle Lo, Asli Celikyilmaz, Yejin Choi
We introduce a general framework for abstractive summarization with factual consistency and distinct modeling of the narrative flow in an output summary.
no code implementations • ACL 2019 • Maarten Sap, Dallas Card, Saadia Gabriel, Yejin Choi, Noah A. Smith
We investigate how annotators{'} insensitivity to differences in dialect can lead to racial bias in automatic hate speech detection models, potentially amplifying harm against minority populations.
no code implementations • NAACL 2019 • Aida Amini, Saadia Gabriel, Peter Lin, Rik Koncel-Kedziorski, Yejin Choi, Hannaneh Hajishirzi
We introduce a new representation language to model precise operation programs corresponding to each math problem that aim to improve both the performance and the interpretability of the learned models.
no code implementations • 21 Nov 2018 • Aaron Walsman, Yonatan Bisk, Saadia Gabriel, Dipendra Misra, Yoav Artzi, Yejin Choi, Dieter Fox
Building perceptual systems for robotics which perform well under tight computational budgets requires novel architectures which rethink the traditional computer vision pipeline.