6 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…
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…
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…
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…
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)…
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…