Today (w/ @ox.ac.uk @stanford @MIT @LSE) we’re sharing the results of the largest AI persuasion experiments to date: 76k participants, 19  LLMs, 707 political issues. We examine “levers” of AI persuasion: model scale, post-training, prompting, personalization, & more!  🧵:

📈Out today in @PNASNews!📈 In a large pre-registered experiment (n=25,982), we find evidence that scaling the size of LLMs yields sharply diminishing persuasive returns for static political messages.  🧵:

Are LLMs biased when they write about political issues? We just released IssueBench – the largest, most realistic benchmark of its kind – to answer this question more robustly than ever before. Long 🧵with spicy results 👇

Everyone going to SJDM this weekend, come to our special session on using #LLMs in #JDM research on Monday at 9:45am (location = Empire Complex)! w/ @kobihackenburg.bsky.social Hope Schroeder and myself - presentations from us but also hopefully lots of discussion/Q&A with all of you!

Labeling misinformation as misleading and from fellow in-group members (e.g., dem/ rep) makes people less likely to share it, suggesting social identity is effective in mitigating misinfo, finds @clarapretus.bsky.social @kobihackenburg.bsky.social @mtsakiris.bsky.social @jayvanbavel.bsky.social