Search Results for author: Burcu Karagol Ayan

Found 7 papers, 3 papers with code

Harm Amplification in Text-to-Image Models

no code implementations1 Feb 2024 Susan Hao, Renee Shelby, Yuchi Liu, Hansa Srinivasan, Mukul Bhutani, Burcu Karagol Ayan, Shivani Poddar, Sarah Laszlo

Text-to-image (T2I) models have emerged as a significant advancement in generative AI; however, there exist safety concerns regarding their potential to produce harmful image outputs even when users input seemingly safe prompts.

Scaling Autoregressive Models for Content-Rich Text-to-Image Generation

2 code implementations22 Jun 2022 Jiahui Yu, Yuanzhong Xu, Jing Yu Koh, Thang Luong, Gunjan Baid, ZiRui Wang, Vijay Vasudevan, Alexander Ku, Yinfei Yang, Burcu Karagol Ayan, Ben Hutchinson, Wei Han, Zarana Parekh, Xin Li, Han Zhang, Jason Baldridge, Yonghui Wu

We present the Pathways Autoregressive Text-to-Image (Parti) model, which generates high-fidelity photorealistic images and supports content-rich synthesis involving complex compositions and world knowledge.

Machine Translation World Knowledge

Which Linguist Invented the Lightbulb? Presupposition Verification for Question-Answering

no code implementations ACL 2021 Najoung Kim, Ellie Pavlick, Burcu Karagol Ayan, Deepak Ramachandran

Through a user preference study, we demonstrate that the oracle behavior of our proposed system that provides responses based on presupposition failure is preferred over the oracle behavior of existing QA systems.

Explanation Generation Natural Questions +1

Text Classification with Few Examples using Controlled Generalization

no code implementations NAACL 2019 Abhijit Mahabal, Jason Baldridge, Burcu Karagol Ayan, Vincent Perot, Dan Roth

Training data for text classification is often limited in practice, especially for applications with many output classes or involving many related classification problems.

General Classification text-classification +2

Context-Based Quotation Recommendation

no code implementations17 May 2020 Ansel MacLaughlin, Tao Chen, Burcu Karagol Ayan, Dan Roth

Our experiments confirm the strong performance of BERT-based methods on this task, which outperform bag-of-words and neural ranking baselines by more than 30% relative across all ranking metrics.

Open-Domain Question Answering

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