8 citations · 9 across the 5 of their papers we have counts for
6 papers
Calibrating LLM Explanations to Human Decisions with Reinforcement Learning
Dong Shu, Ziyang Guo, Negar Kamali +2
Explanations of LLM outputs are considered an important way to make model behavior legible, helping human users make good decisions on downstream tasks. However, default training a…
Generative AI Literacy Training Improves Intelligence Analysts' Discrimination of Real and AI-Generated Images
Negar Kamali, Candice Rockell Gerstner, Jessica Hullman +1
Across social and online platforms, people are increasingly exposed to AI-generated images. As a consequence, the task of distinguishing AI-generated from authentic images is becom…
Practicing with Language Models Cultivates Human Empathic Communication
Aakriti Kumar, Nalin Poungpeth, Diyi Yang +2
Empathy is central to human connection, yet people often struggle to express it effectively. In blinded evaluations, large language models (LLMs) generate responses that are often…
When Large Language Models are Reliable for Judging Empathic Communication
Aakriti Kumar, Nalin Poungpeth, Diyi Yang +3
Large language models (LLMs) excel at generating empathic responses in text-based conversations. But, how reliably do they judge the nuances of empathic communication? We investiga…
Characterizing Photorealism and Artifacts in Diffusion Model-Generated Images
Negar Kamali, Karyn Nakamura, Aakriti Kumar +3
Diffusion model-generated images can appear indistinguishable from authentic photographs, but these images often contain artifacts and implausibilities that reveal their AI-generat…
How to Distinguish AI-Generated Images from Authentic Photographs
Negar Kamali, Karyn Nakamura, Angelos Chatzimparmpas +2
The high level of photorealism in state-of-the-art diffusion models like Midjourney, Stable Diffusion, and Firefly makes it difficult for untrained humans to distinguish between re…