25 citations · 53 across the 7 of their papers we have counts for
9 papers
Information Discernment in Large Language Models
Joshua Ashkinaze, Laura Kurek, Alina Faisal +4
LLMs are increasingly used with external knowledge sources like the internet. Do they weigh information appropriately -- updating more for reliable sources (source discernment) and…
Follow Nudges without Budges: A Field Experiment on Misinformation Followers Didn't Change Follow Networks
Laura Kurek, Joshua Ashkinaze, Ceren Budak +1
Can digital ads encourage users exposed to inaccurate information sources to follow accurate ones? We conduct a large-scale field experiment (N=28,582) on X, formerly Twitter, with…
Deep Value Benchmark: Measuring Whether Models Generalize Deep Values or Shallow Preferences
Joshua Ashkinaze, Hua Shen, Saipranav Avula +2
We introduce the Deep Value Benchmark (DVB), an evaluation framework that directly tests whether large language models (LLMs) learn fundamental human values or merely surface-level…
Reporting Non-Consensual Intimate Media: An Audit Study of Deepfakes
Li Qiwei, Shihui Zhang, Andrew Timothy Kasper +4
Non-consensual intimate media (NCIM) inflicts significant harm. Currently, victim-survivors can use two mechanisms to report NCIM - as a non-consensual nudity violation or as copyr…
Plurals: A System for Guiding LLMs Via Simulated Social Ensembles
Joshua Ashkinaze, Emily Fry, Narendra Edara +2
Recent debates raised concerns that language models may favor certain viewpoints. But what if the solution is not to aim for a 'view from nowhere' but rather to leverage different…
Seeing Like an AI: How LLMs Apply (and Misapply) Wikipedia Neutrality Norms
Joshua Ashkinaze, Ruijia Guan, Laura Kurek +3
Large language models (LLMs) are trained on broad corpora and then used in communities with specialized norms. Is providing LLMs with community rules enough for models to follow th…
Prompt Design Matters for Computational Social Science Tasks but in Unpredictable Ways
Shubham Atreja, Joshua Ashkinaze, Lingyao Li +2
Manually annotating data for computational social science tasks can be costly, time-consuming, and emotionally draining. While recent work suggests that LLMs can perform such annot…
The Dynamics of (Not) Unfollowing Misinformation Spreaders
Joshua Ashkinaze, Eric Gilbert, Ceren Budak
Many studies explore how people 'come into' misinformation exposure. But much less is known about how people 'come out of' misinformation exposure. Do people organically sever ties…
How AI Ideas Affect the Creativity, Diversity, and Evolution of Human Ideas: Evidence From a Large, Dynamic Experiment
Joshua Ashkinaze, Julia Mendelsohn, Li Qiwei +2
Exposure to large language model output is rapidly increasing. How will seeing AI-generated ideas affect human ideas? We conducted an experiment (800+ participants, 40+ countries)…