LLM-judge human-likeness scores place our BN target near the human reference and above baselines.
Bottom line: endpoint movement alone is not enough.
Process fidelity matters: when beliefs move, how they move, and whether simulator updates look human.
Code: github.com/jlcmoore/per...
Preprint: arxiv.org/abs/2606.05330
This is work with:
@noahdgoodman.bsky.social Nick Haber @maxkw.bsky.social
Naive responsiveness asks if trivial arguments (repeating the proposition over and over) move the target too much. Only our BN resists trivial persuasion, while both LLM targets overreact to it.
Stance bias asks whether a simulator is much easier to move in one stance direction than the other. This matched for-versus-against asymmetry is lowest for our BN target, indicating less stance-dependent bias than baselines.
People show different belief traces and rhetorical susceptibility. We see two trajectory regimes: some people barely move, while others shift substantially early on and then partially drift back. We find that ethos is negatively associated with persuasion delta.
We then build a probabilistic simulator of human persuadability. It compares an unstructured LLM target, a structure-conditioned LLM target, and a Bayesian-network target with explicit latent belief-state updates each turn.
Interested in how AI is affecting people?
Please sign up to review a few papers for our proposed NeurIPS 2026 workshop on Measurement and Models of Psychological Impact (Sydney, Dec 12).
Sign up: forms.gle/4v3KiKKAmzgX...
LLMs can shift people's beliefs.
But most persuasion studies only check beliefs before and after a conversation.
We built PersuasionTrace to measure beliefs turn by turn, so we can study how belief updates actually unfold.
Organized by:
Marwa Abdulhai @lujain.bsky.social Andreas Haupt, Pattie Maes, Ashish Mehta, Micaela Rodriguez, Max Kleiman-Weiner
Confirmed speakers include:
Jina Suh, Tom Griffiths, @micahcarroll.bsky.social @desmond-ong.bsky.social @hannahrosekirk.bsky.social @brianchristian.bsky.social
We built a human-participant-facing web platform for AI persuasion experiments that supports multi-turn belief tracing, audio I/O, and participant-chosen propositions. Using it, we show LLMs can persuade across standard text, personalized text, and audio.