15 citations · 26 across the 6 of their papers we have counts for
6 papers
Deepfake-Eval-2024: A Multi-Modal In-the-Wild Benchmark of Deepfakes Circulated in 2024
Nuria Alina Chandra, Hannah Lee, Ryan Murtfeldt +10
In the age of increasingly realistic generative AI, robust deepfake detection is essential for mitigating fraud and disinformation. While many deepfake detectors report high accura…
DebiasPI: Inference-time Debiasing by Prompt Iteration of a Text-to-Image Generative Model
Sarah Bonna, Yu-Cheng Huang, Ekaterina Novozhilova +11
Ethical intervention prompting has emerged as a tool to counter demographic biases of text-to-image generative AI models. Existing solutions either require to retrain the model or…
Enhancing Emotion Prediction in News Headlines: Insights from ChatGPT and Seq2Seq Models for Free-Text Generation
Ge Gao, Jongin Kim, Sejin Paik +5
Predicting emotions elicited by news headlines can be challenging as the task is largely influenced by the varying nature of people's interpretations and backgrounds. Previous work…
RIP Twitter API: A eulogy to its vast research contributions
Ryan Murtfeldt, Sejin Paik, Naomi Alterman +2
Since 2006, Twitter's APIs have been rich sources of data for researchers studying social phenomena such as misinformation, public communication, crisis response, and political beh…
On Measuring Social Biases in Prompt-Based Multi-Task Learning
Afra Feyza Akyürek, Sejin Paik, Muhammed Yusuf Kocyigit +3
Large language models trained on a mixture of NLP tasks that are converted into a text-to-text format using prompts, can generalize into novel forms of language and handle novel ta…
Challenges in Measuring Bias via Open-Ended Language Generation
Afra Feyza Akyürek, Muhammed Yusuf Kocyigit, Sejin Paik +1
Researchers have devised numerous ways to quantify social biases vested in pretrained language models. As some language models are capable of generating coherent completions given…